diff --git a/example/ck_tile/38_block_scale_gemm/CMakeLists.txt b/example/ck_tile/38_block_scale_gemm/CMakeLists.txt
index 12cf874c73..d9a6c2bac0 100644
--- a/example/ck_tile/38_block_scale_gemm/CMakeLists.txt
+++ b/example/ck_tile/38_block_scale_gemm/CMakeLists.txt
@@ -6,8 +6,12 @@ endif()
 list(APPEND EXAMPLE_GEMM_COMPILE_OPTIONS -mllvm -enable-noalias-to-md-conversion=0)
 
 if(GPU_TARGETS MATCHES "gfx94" OR GPU_TARGETS MATCHES "gfx95")
-    add_executable(tile_example_gemm_aquant_basic EXCLUDE_FROM_ALL gemm_aquant_basic.cpp)
-    target_compile_options(tile_example_gemm_aquant_basic PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
+    add_executable(tile_example_gemm_quant_basic EXCLUDE_FROM_ALL gemm_quant_basic.cpp)
+    target_compile_options(tile_example_gemm_quant_basic PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
+
+    add_executable(tile_example_gemm_aquant_preshuffle EXCLUDE_FROM_ALL gemm_aquant_preshuffle.cpp)
+    target_compile_options(tile_example_gemm_aquant_preshuffle PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
+
     add_executable(tile_example_gemm_bquant_basic EXCLUDE_FROM_ALL gemm_bquant_basic.cpp)
     target_compile_options(tile_example_gemm_bquant_basic PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
 else()
diff --git a/example/ck_tile/38_block_scale_gemm/README.md b/example/ck_tile/38_block_scale_gemm/README.md
index 6d6aec28c8..36f7995c14 100644
--- a/example/ck_tile/38_block_scale_gemm/README.md
+++ b/example/ck_tile/38_block_scale_gemm/README.md
@@ -1,18 +1,21 @@
-# GEMM Matrix Multiplication
+# Quant GEMM Matrix Multiplication
 
-This folder contains example for Block Scale GEMM using ck_tile tile-programming implementation. 
+This folder contains examples of quant GEMMs using the ck_tile tile-programming implementation.
+
+- AQuant kernel with blocks of A matrix sharing scales: custom GEMM pipeline
+- Row and Column-wise scaled: scaling implemented in Epilogue
 
 ## build
 ```
 # in the root of ck_tile
 mkdir build && cd build
-# you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank
+# you can replace <arch> with the appropriate architecture (for example gfx942) or leave it blank
 ../script/cmake-ck-dev.sh  ../ <arch>
-# The aquant pipeline method on the gemm calculation
-make tile_example_gemm_aquant_basic -j
+# Compile the quant kernels
+make tile_example_gemm_quant_basic -j
 make tile_example_gemm_bquant_basic -j
 ```
-This will result in an executable `build/bin/tile_example_gemm_aquant_basic`
+This will result in an executable `build/bin/tile_example_gemm_quant_basic`
 
 ## example
 ```
@@ -22,15 +25,16 @@ args:
           -n    n dimension (default:2048)
           -k    k dimension (default:64)
    -a_layout    Tensor A data layout (default: R)
-   -b_layout    Tensor B data layout (default: R)
+   -b_layout    Tensor B data layout (default: C)
    -c_layout    Tensor C data layout (default: R)
    -stride_a    Tensor A stride (default:0)
    -stride_b    Tensor B stride (default:0)
    -stride_c    Tensor C stride (default:0)
-          -v    0. No validation, 1. Validation on CPU, 2. Validation on GPU (default:2)
+          -v    0. No validation, 1. Validation on CPU, 2. Validation on GPU (default:1)
           -e    Absolute error tolerance (default:1e-5)
-       -prec    data type. fp16/bf16/fp8/bf8/int8 (default:fp16)
+       -prec    data type. fp8/bf8/i4fp8/i4bf8/i4f32fp8/i4f32bf8 (default:fp8)
      -warmup    number of iterations before benchmark the kernel (default:10)
      -repeat    number of iterations to benchmark the kernel (default:100)
       -timer    gpu:gpu timer, cpu:cpu timer (default:gpu)
+ -quant_mode    Which quant method to use (aquant, rowcol)
 ```
diff --git a/example/ck_tile/38_block_scale_gemm/gemm_aquant_basic.cpp b/example/ck_tile/38_block_scale_gemm/gemm_aquant_basic.cpp
deleted file mode 100644
index d5a38fe754..0000000000
--- a/example/ck_tile/38_block_scale_gemm/gemm_aquant_basic.cpp
+++ /dev/null
@@ -1,226 +0,0 @@
-// SPDX-License-Identifier: MIT
-// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
-
-#include <cstring>
-#include <iostream>
-#include <ostream>
-#include <stdexcept>
-#include <string>
-#include <tuple>
-
-#include "gemm_utils.hpp"
-
-template <typename GemmConfig,
-          typename ADataType,
-          typename AQDataType,
-          typename BDataType,
-          typename AccDataType,
-          typename CDataType,
-          typename ComputeDataType,
-          typename ALayout,
-          typename BLayout,
-          typename CLayout,
-          uint32_t QuantGroupSize>
-float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::stream_config& s)
-{
-    constexpr bool kPadM = false;
-    constexpr bool kPadN = false;
-    constexpr bool kPadK = false;
-
-    static_assert(std::is_same_v<CLayout, ck_tile::tensor_layout::gemm::RowMajor>);
-
-    constexpr ck_tile::index_t M_Tile = GemmConfig::M_Tile;
-    constexpr ck_tile::index_t N_Tile = GemmConfig::N_Tile;
-    constexpr ck_tile::index_t K_Tile = GemmConfig::K_Tile;
-
-    constexpr ck_tile::index_t M_Warp = GemmConfig::M_Warp;
-    constexpr ck_tile::index_t N_Warp = GemmConfig::N_Warp;
-    constexpr ck_tile::index_t K_Warp = GemmConfig::K_Warp;
-
-    constexpr ck_tile::index_t M_Warp_Tile = GemmConfig::M_Warp_Tile;
-    constexpr ck_tile::index_t N_Warp_Tile = GemmConfig::N_Warp_Tile;
-    constexpr ck_tile::index_t K_Warp_Tile = GemmConfig::K_Warp_Tile;
-
-    using CodegenGemmShape =
-        ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
-                               ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
-                               ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
-
-    using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenGemmShape>;
-
-    using CodegenGemmTraits = ck_tile::TileGemmAQuantTraits<kPadM,
-                                                            kPadN,
-                                                            kPadK,
-                                                            GemmConfig::PreshuffleQuant,
-                                                            ALayout,
-                                                            BLayout,
-                                                            CLayout>;
-
-    using GemmPipelineProblem = ck_tile::GemmPipelineProblemBase<ADataType,
-                                                                 BDataType,
-                                                                 AccDataType,
-                                                                 CodegenGemmShape,
-                                                                 CodegenGemmTraits,
-                                                                 ComputeDataType>;
-
-    using BaseGemmPipeline = ck_tile::BaseAQuantGemmPipelineAgBgCrCompV3<GemmPipelineProblem>;
-
-    const ck_tile::index_t K_split      = (args.K + K_Tile - 1) / K_Tile * K_Tile;
-    const ck_tile::index_t num_loop     = TilePartitioner::GetLoopNum(K_split);
-    const bool has_hot_loop             = BaseGemmPipeline::BlockHasHotloop(num_loop);
-    const ck_tile::TailNumber tail_num  = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
-    constexpr bool transposed_warp_gemm = true;
-
-    const auto Run = [&](const auto has_hot_loop_, const auto tail_number_) {
-        constexpr bool has_hot_loop_v = has_hot_loop_.value;
-        constexpr auto tail_number_v  = tail_number_.value;
-
-        using CodegenPipelineProblem =
-            ck_tile::GemmAQuantPipelineProblem<ADataType,
-                                               AQDataType,
-                                               BDataType,
-                                               AccDataType,
-                                               CodegenGemmShape,
-                                               CodegenGemmTraits,
-                                               QuantGroupSize,
-                                               transposed_warp_gemm,
-                                               ComputeDataType,
-                                               ck_tile::GemmPipelineScheduler::Intrawave,
-                                               has_hot_loop_v,
-                                               tail_number_v>;
-        using CodegenGemmPipeline = ck_tile::AQuantGemmPipelineAgBgCrCompV3<CodegenPipelineProblem>;
-        using GemmEpilogue        = ck_tile::CShuffleEpilogue<
-                   ck_tile::CShuffleEpilogueProblem<ADataType,
-                                                    BDataType,
-                                                    ck_tile::tuple<>,
-                                                    AccDataType,
-                                                    CDataType,
-                                                    ck_tile::tuple<>,
-                                                    CLayout,
-                                                    ck_tile::element_wise::PassThrough,
-                                                    TilePartitioner::MPerBlock,
-                                                    TilePartitioner::NPerBlock,
-                                                    M_Warp,
-                                                    N_Warp,
-                                                    M_Warp_Tile,
-                                                    N_Warp_Tile,
-                                                    K_Warp_Tile,
-                                                    transposed_warp_gemm,
-                                                    ck_tile::memory_operation_enum::set>>;
-        using Kernel =
-            ck_tile::AQuantGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
-
-        auto kargs = Kernel::MakeKernelArgs(args);
-
-        const dim3 grids  = Kernel::GridSize(args.M, args.N, args.k_batch);
-        const dim3 blocks = Kernel::BlockSize();
-
-        if(args.k_batch != 1)
-        {
-            throw std::runtime_error("split-k is not supported yet!");
-        }
-
-        if(!Kernel::IsSupportedArgument(kargs))
-        {
-            throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
-        }
-
-        if(s.log_level_ > 0)
-        {
-            std::cout << "Launching kernel with args: " << Kernel::GetName() << '\n'
-                      << "shape: " << CodegenGemmShape::GetName() << '\n'
-                      << "problem: " << CodegenPipelineProblem::GetName() << '\n'
-                      << "pipeline: " << CodegenGemmPipeline::GetName() << '\n'
-                      << "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
-                      << ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z << "}"
-                      << std::endl;
-        }
-
-        float ave_time = ck_tile::launch_kernel(
-            s, ck_tile::make_kernel<GemmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
-
-        return ave_time;
-    };
-    return BaseGemmPipeline::TailHandler(Run, has_hot_loop, tail_num);
-}
-
-#include "run_gemm_aquant_example.inc"
-
-template <typename GemmConfig, typename TypeConfig, uint32_t QuantGroupSize>
-int run_gemm_example_prec_type(std::string a_layout, std::string b_layout, int argc, char* argv[])
-{
-    using Row = ck_tile::tensor_layout::gemm::RowMajor;
-    using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
-
-    if constexpr(std::is_same_v<typename TypeConfig::ADataType, ck_tile::pk_int4_t> ||
-                 std::is_same_v<typename TypeConfig::ADataType, ck_tile::fp8_t> ||
-                 std::is_same_v<typename TypeConfig::ADataType, ck_tile::bf8_t>)
-    {
-        if(a_layout == "R" && b_layout == "C")
-        {
-            return run_gemm_example_with_layouts<GemmConfig, TypeConfig, QuantGroupSize>(
-                argc, argv, Row{}, Row{}, Col{}, Row{});
-        }
-        else
-        {
-            throw std::runtime_error("Unsupported memory layout for the input matrices!");
-        }
-    }
-    else
-    {
-        throw std::runtime_error("Unsupported data type for A.");
-    }
-
-    return 0;
-}
-
-template <template <typename PreType> typename GemmConfig>
-int run_gemm_example(int argc, char* argv[])
-{
-    auto [result, arg_parser] = create_args(argc, argv);
-    if(!result)
-        return -1;
-
-    std::string data_type = arg_parser.get_str("prec");
-    std::string a_layout  = arg_parser.get_str("a_layout");
-    std::string b_layout  = arg_parser.get_str("b_layout");
-
-    if(data_type == "fp8")
-    {
-        using TypeConfig =
-            decltype(GemmQuantTypeConfig<ck_tile::fp8_t, ck_tile::fp8_t, ck_tile::half_t, float>{});
-        return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>, TypeConfig, 128>(
-            a_layout, b_layout, argc, argv);
-    }
-    else if(data_type == "bf8")
-    {
-        using TypeConfig =
-            decltype(GemmQuantTypeConfig<ck_tile::bf8_t, ck_tile::bf8_t, ck_tile::half_t, float>{});
-        return run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t>, TypeConfig, 128>(
-            a_layout, b_layout, argc, argv);
-    }
-    else if(data_type == "i4fp8")
-    {
-        using TypeConfig = decltype(GemmQuantTypeConfig<ck_tile::pk_int4_t,
-                                                        ck_tile::fp8_t,
-                                                        ck_tile::half_t,
-                                                        ck_tile::fp8_t>{});
-        return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>, TypeConfig, 128>(
-            a_layout, b_layout, argc, argv);
-    }
-    else if(data_type == "i4bf8")
-    {
-        using TypeConfig = decltype(GemmQuantTypeConfig<ck_tile::pk_int4_t,
-                                                        ck_tile::bf8_t,
-                                                        ck_tile::half_t,
-                                                        ck_tile::bf8_t>{});
-        return run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t>, TypeConfig, 128>(
-            a_layout, b_layout, argc, argv);
-    }
-    else
-    {
-        throw std::runtime_error("Unsupported data type for this operation !!!");
-    }
-}
-
-int main(int argc, char* argv[]) { return !run_gemm_example<GemmConfigDecode>(argc, argv); }
diff --git a/example/ck_tile/38_block_scale_gemm/gemm_aquant_preshuffle.cpp b/example/ck_tile/38_block_scale_gemm/gemm_aquant_preshuffle.cpp
index 13c416110a..799e4a0e73 100644
--- a/example/ck_tile/38_block_scale_gemm/gemm_aquant_preshuffle.cpp
+++ b/example/ck_tile/38_block_scale_gemm/gemm_aquant_preshuffle.cpp
@@ -21,7 +21,7 @@ template <typename GemmConfig,
           typename BLayout,
           typename CLayout,
           uint32_t QuantGroupSize>
-float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::stream_config& s)
+float gemm_calc_aquant(const ck_tile::QuantGemmHostArgs& args, const ck_tile::stream_config& s)
 {
     constexpr bool kPadM = false;
     constexpr bool kPadN = false;
@@ -50,13 +50,14 @@ float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::s
 
     using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenGemmShape>;
 
-    using CodegenGemmTraits = ck_tile::TileGemmAQuantTraits<kPadM,
-                                                            kPadN,
-                                                            kPadK,
-                                                            GemmConfig::PreshuffleQuant,
-                                                            ALayout,
-                                                            BLayout,
-                                                            CLayout>;
+    using CodegenGemmTraits = ck_tile::TileGemmQuantTraits<kPadM,
+                                                           kPadN,
+                                                           kPadK,
+                                                           GemmConfig::PreshuffleQuant,
+                                                           ALayout,
+                                                           BLayout,
+                                                           CLayout,
+                                                           ck_tile::QuantType::AQuantGrouped>;
 
     using GemmPipelineProblem = ck_tile::GemmPipelineProblemBase<ADataType,
                                                                  BDataType,
@@ -109,8 +110,10 @@ float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::s
                                                     K_Warp_Tile,
                                                     transposed_warp_gemm,
                                                     ck_tile::memory_operation_enum::set>>;
-        using Kernel =
-            ck_tile::AQuantGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
+        using Kernel = ck_tile::QuantGemmKernel<TilePartitioner,
+                                                CodegenGemmPipeline,
+                                                GemmEpilogue,
+                                                ck_tile::QuantType::AQuantGrouped>;
 
         auto kargs = Kernel::MakeKernelArgs(args);
 
diff --git a/example/ck_tile/38_block_scale_gemm/gemm_bquant_basic.cpp b/example/ck_tile/38_block_scale_gemm/gemm_bquant_basic.cpp
index 49e60bf86d..f4e63d54da 100644
--- a/example/ck_tile/38_block_scale_gemm/gemm_bquant_basic.cpp
+++ b/example/ck_tile/38_block_scale_gemm/gemm_bquant_basic.cpp
@@ -23,7 +23,7 @@ template <typename GemmConfig,
           typename BLayout,
           typename CLayout,
           uint32_t QuantGroupSize>
-float gemm_calc_bquant(const ck_tile::BQuantGemmHostArgs& args, const ck_tile::stream_config& s)
+float gemm_calc_bquant(const ck_tile::QuantGemmHostArgs& args, const ck_tile::stream_config& s)
 {
     constexpr bool kPadM = false;
     constexpr bool kPadN = false;
@@ -50,13 +50,14 @@ float gemm_calc_bquant(const ck_tile::BQuantGemmHostArgs& args, const ck_tile::s
 
     using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenGemmShape>;
 
-    using CodegenGemmTraits = ck_tile::TileGemmBQuantTraits<kPadM,
-                                                            kPadN,
-                                                            kPadK,
-                                                            GemmConfig::PreshuffleQuant,
-                                                            ALayout,
-                                                            BLayout,
-                                                            CLayout>;
+    using CodegenGemmTraits = ck_tile::TileGemmQuantTraits<kPadM,
+                                                           kPadN,
+                                                           kPadK,
+                                                           GemmConfig::PreshuffleQuant,
+                                                           ALayout,
+                                                           BLayout,
+                                                           CLayout,
+                                                           ck_tile::QuantType::BQuantGrouped>;
 
     using GemmPipelineProblem = ck_tile::GemmPipelineProblemBase<ADataType,
                                                                  BDataType,
@@ -108,8 +109,10 @@ float gemm_calc_bquant(const ck_tile::BQuantGemmHostArgs& args, const ck_tile::s
                                                     K_Warp_Tile,
                                                     transposed_warp_gemm,
                                                     ck_tile::memory_operation_enum::set>>;
-        using Kernel =
-            ck_tile::BQuantGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
+        using Kernel = ck_tile::QuantGemmKernel<TilePartitioner,
+                                                CodegenGemmPipeline,
+                                                GemmEpilogue,
+                                                ck_tile::QuantType::BQuantGrouped>;
 
         auto kargs = Kernel::MakeKernelArgs(args);
 
diff --git a/example/ck_tile/38_block_scale_gemm/gemm_quant_basic.cpp b/example/ck_tile/38_block_scale_gemm/gemm_quant_basic.cpp
new file mode 100644
index 0000000000..5345686e20
--- /dev/null
+++ b/example/ck_tile/38_block_scale_gemm/gemm_quant_basic.cpp
@@ -0,0 +1,376 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <cstring>
+#include <iostream>
+#include <ostream>
+#include <stdexcept>
+#include <string>
+#include <tuple>
+
+#include "ck_tile/core/config.hpp"
+#include "ck_tile/host.hpp"
+#include "gemm_utils.hpp"
+
+template <typename GemmConfig,
+          typename TypeConfig,
+          typename ALayout,
+          typename BLayout,
+          typename CLayout,
+          uint32_t QuantGroupSize,
+          ck_tile::QuantType QuantMode>
+float gemm_calc_quant(const ck_tile::QuantGemmHostArgs& args, const ck_tile::stream_config& s)
+{
+    constexpr bool kPadM = GemmConfig::kPadM;
+    constexpr bool kPadN = GemmConfig::kPadN;
+    constexpr bool kPadK = GemmConfig::kPadK;
+
+    constexpr int kBlockPerCu = GemmConfig::kBlockPerCu;
+
+    static_assert(std::is_same_v<CLayout, ck_tile::tensor_layout::gemm::RowMajor>);
+
+    constexpr ck_tile::index_t M_Tile = GemmConfig::M_Tile;
+    constexpr ck_tile::index_t N_Tile = GemmConfig::N_Tile;
+    constexpr ck_tile::index_t K_Tile = GemmConfig::K_Tile;
+
+    constexpr ck_tile::index_t M_Warp = GemmConfig::M_Warp;
+    constexpr ck_tile::index_t N_Warp = GemmConfig::N_Warp;
+    constexpr ck_tile::index_t K_Warp = GemmConfig::K_Warp;
+
+    constexpr ck_tile::index_t M_Warp_Tile = GemmConfig::M_Warp_Tile;
+    constexpr ck_tile::index_t N_Warp_Tile = GemmConfig::N_Warp_Tile;
+    constexpr ck_tile::index_t K_Warp_Tile = GemmConfig::K_Warp_Tile;
+
+    // B datatype is safe to use as compute type as it should be at least fp8
+    using ComputeDataType = typename TypeConfig::BDataType;
+
+    using CodegenGemmShape =
+        ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
+                               ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
+                               ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
+
+    using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenGemmShape>;
+
+    using CodegenGemmTraits = ck_tile::TileGemmQuantTraits<kPadM,
+                                                           kPadN,
+                                                           kPadK,
+                                                           GemmConfig::PreshuffleQuant,
+                                                           ALayout,
+                                                           BLayout,
+                                                           CLayout,
+                                                           QuantMode>;
+
+    using GemmPipelineProblem = ck_tile::GemmPipelineProblemBase<typename TypeConfig::ADataType,
+                                                                 typename TypeConfig::BDataType,
+                                                                 typename TypeConfig::AccDataType,
+                                                                 CodegenGemmShape,
+                                                                 CodegenGemmTraits,
+                                                                 ComputeDataType>;
+
+    using BaseGemmPipeline = ck_tile::BaseGemmPipelineAgBgCrCompV3<GemmPipelineProblem>;
+
+    const ck_tile::index_t K_split      = (args.K + K_Tile - 1) / K_Tile * K_Tile;
+    const ck_tile::index_t num_loop     = TilePartitioner::GetLoopNum(K_split);
+    const bool has_hot_loop             = BaseGemmPipeline::BlockHasHotloop(num_loop);
+    const ck_tile::TailNumber tail_num  = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
+    constexpr bool transposed_warp_gemm = false;
+
+    const auto Run = [&](const auto has_hot_loop_, const auto tail_number_) {
+        constexpr bool has_hot_loop_v = has_hot_loop_.value;
+        constexpr auto tail_number_v  = tail_number_.value;
+        constexpr bool transpose_c    = false;
+
+        using CodegenPipelineProblem = std::conditional_t<
+            QuantMode == ck_tile::QuantType::AQuantGrouped,
+            ck_tile::GemmAQuantPipelineProblem<typename TypeConfig::ADataType,
+                                               typename TypeConfig::QDataType,
+                                               typename TypeConfig::BDataType,
+                                               typename TypeConfig::AccDataType,
+                                               CodegenGemmShape,
+                                               CodegenGemmTraits,
+                                               QuantGroupSize,
+                                               transpose_c,
+                                               ComputeDataType,
+                                               ck_tile::GemmPipelineScheduler::Intrawave,
+                                               has_hot_loop_v,
+                                               tail_number_v>,
+            ck_tile::GemmRowColQuantPipelineProblem<typename TypeConfig::ADataType,
+                                                    typename TypeConfig::BDataType,
+                                                    typename TypeConfig::AccDataType,
+                                                    typename TypeConfig::AccDataType,
+                                                    CodegenGemmShape,
+                                                    CodegenGemmTraits,
+                                                    transpose_c,
+                                                    ComputeDataType,
+                                                    ck_tile::GemmPipelineScheduler::Intrawave,
+                                                    has_hot_loop_v,
+                                                    tail_number_v>>;
+
+        using CodegenGemmPipeline =
+            std::conditional_t<QuantMode == ck_tile::QuantType::AQuantGrouped,
+                               ck_tile::AQuantGemmPipelineAgBgCrCompV3<CodegenPipelineProblem>,
+                               ck_tile::GemmPipelineAgBgCrCompV3<CodegenPipelineProblem>>;
+
+        using GemmEpilogue = ck_tile::CShuffleEpilogue<
+            ck_tile::CShuffleEpilogueProblem<typename TypeConfig::ADataType,
+                                             typename TypeConfig::BDataType,
+                                             ck_tile::tuple<>,
+                                             typename TypeConfig::AccDataType,
+                                             typename TypeConfig::CDataType,
+                                             ck_tile::tuple<>,
+                                             CLayout,
+                                             ck_tile::element_wise::PassThrough,
+                                             TilePartitioner::MPerBlock,
+                                             TilePartitioner::NPerBlock,
+                                             M_Warp,
+                                             N_Warp,
+                                             M_Warp_Tile,
+                                             N_Warp_Tile,
+                                             K_Warp_Tile,
+                                             transposed_warp_gemm,
+                                             ck_tile::memory_operation_enum::set>>;
+        using Kernel =
+            ck_tile::QuantGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue, QuantMode>;
+
+        auto kargs = Kernel::MakeKernelArgs(args);
+
+        const dim3 grids  = Kernel::GridSize(args.M, args.N, args.k_batch);
+        const dim3 blocks = Kernel::BlockSize();
+
+        if(args.k_batch != 1)
+        {
+            throw std::runtime_error("split-k is not supported yet!");
+        }
+
+        if(!Kernel::IsSupportedArgument(kargs))
+        {
+            throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
+        }
+
+        if(s.log_level_ > 0)
+        {
+            std::cout << "Launching kernel with args: " << Kernel::GetName() << '\n'
+                      << "shape: " << CodegenGemmShape::GetName() << '\n'
+                      << "problem: " << CodegenPipelineProblem::GetName() << '\n'
+                      << "pipeline: " << CodegenGemmPipeline::GetName() << '\n'
+                      << "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
+                      << ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z << "}"
+                      << std::endl;
+        }
+
+        float ave_time = ck_tile::launch_kernel(
+            s, ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
+
+        return ave_time;
+    };
+    return BaseGemmPipeline::TailHandler(Run, has_hot_loop, tail_num);
+}
+
+#include "run_gemm_quant_example.inc"
+
+template <typename GemmConfig,
+          typename TypeConfig,
+          uint32_t QuantGroupSize,
+          ck_tile::QuantType QuantMode>
+int run_gemm_example_prec_type(std::string a_layout, std::string b_layout, int argc, char* argv[])
+{
+    using Row = ck_tile::tensor_layout::gemm::RowMajor;
+    using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
+
+    if constexpr(std::is_same_v<typename TypeConfig::ADataType, ck_tile::pk_int4_t> ||
+                 std::is_same_v<typename TypeConfig::ADataType, ck_tile::fp8_t> ||
+                 std::is_same_v<typename TypeConfig::ADataType, ck_tile::bf8_t>)
+    {
+        if(a_layout == "R" && b_layout == "C")
+        {
+            return run_gemm_example_with_layouts<GemmConfig, TypeConfig, QuantGroupSize, QuantMode>(
+                argc, argv, Row{}, Row{}, Col{}, Col{}, Row{});
+        }
+        else
+        {
+            throw std::runtime_error("Unsupported memory layout for the input matrices!");
+        }
+    }
+    else
+    {
+        throw std::runtime_error("Unsupported data type for A.");
+    }
+
+    return 0;
+}
+
+void print_help(const char* program_name)
+{
+    std::cout
+        << "Usage: " << program_name << " [OPTIONS]\n"
+        << "\n Parameters:\n"
+        << "  -quant_mode=MODE     aquant (A quantization) or rowcol (row/column quantization)\n"
+        << "  -v=LEVEL              0=No validation, 1=CPU validation, 2=GPU validation\n"
+        << "  -prec=TYPE           Data types: fp8, bf8, i4fp8, i4bf8, i4f32fp8, i4f32bf8\n"
+        << "  -m, -n, -k=SIZE       Matrix dimensions (M×K) @ (K×N) = (M×N)\n"
+        << "\nExample:\n"
+        << "  " << program_name << " -quant_mode=rowcol -v=1 -prec=fp8\n"
+        << std::endl;
+}
+
+template <template <typename PreType> typename GemmConfig>
+int run_gemm_example(int argc, char* argv[])
+{
+
+    for(int i = 1; i < argc; ++i)
+    {
+        if(strcmp(argv[i], "--help") == 0 || strcmp(argv[i], "-h") == 0)
+        {
+            print_help(argv[0]);
+            return 0;
+        }
+    }
+
+    auto [result, arg_parser] = create_args(argc, argv);
+    if(!result)
+        return -1;
+
+    std::string data_type = arg_parser.get_str("prec");
+    std::string a_layout  = arg_parser.get_str("a_layout");
+    std::string b_layout  = arg_parser.get_str("b_layout");
+
+    std::string quant_mode = arg_parser.get_str("quant_mode");
+
+    if(data_type == "fp8")
+    {
+        using TypeConfig =
+            decltype(GemmQuantTypeConfig<ck_tile::fp8_t, ck_tile::fp8_t, ck_tile::half_t>{});
+
+        if(quant_mode == "aquant")
+        {
+            return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>,
+                                              TypeConfig,
+                                              128,
+                                              ck_tile::QuantType::AQuantGrouped>(
+                a_layout, b_layout, argc, argv);
+        }
+        else if(quant_mode == "rowcol")
+        {
+            return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>,
+                                              TypeConfig,
+                                              128,
+                                              ck_tile::QuantType::RowColQuant>(
+                a_layout, b_layout, argc, argv);
+        }
+        else
+        {
+            throw std::runtime_error("Unsupported quantization mode! Use 'aquant' or 'rowcol'");
+        }
+    }
+    else if(data_type == "bf8")
+    {
+        using TypeConfig = decltype(GemmQuantTypeConfig<ck_tile::bf8_t, ck_tile::bf8_t, float>{});
+
+        if(quant_mode == "aquant")
+        {
+            return run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t>,
+                                              TypeConfig,
+                                              128,
+                                              ck_tile::QuantType::AQuantGrouped>(
+                a_layout, b_layout, argc, argv);
+        }
+        else if(quant_mode == "rowcol")
+        {
+            return run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t>,
+                                              TypeConfig,
+                                              128,
+                                              ck_tile::QuantType::RowColQuant>(
+                a_layout, b_layout, argc, argv);
+        }
+        else
+        {
+            throw std::runtime_error("Unsupported quantization mode! Use 'aquant' or 'rowcol'");
+        }
+    }
+    else if(data_type == "i4fp8")
+    {
+        using TypeConfig = decltype(GemmQuantTypeConfig<ck_tile::pk_int4_t,
+                                                        ck_tile::fp8_t,
+                                                        float,
+                                                        ck_tile::fp8_t>{});
+
+        if(quant_mode == "aquant")
+        {
+            return run_gemm_example_prec_type<GemmConfig<ck_tile::pk_int4_t>,
+                                              TypeConfig,
+                                              128,
+                                              ck_tile::QuantType::AQuantGrouped>(
+                a_layout, b_layout, argc, argv);
+        }
+        else
+        {
+            throw std::runtime_error(
+                "Unsupported quantization mode for this datatype! Use 'aquant'.");
+        }
+    }
+    else if(data_type == "i4bf8")
+    {
+        using TypeConfig = decltype(GemmQuantTypeConfig<ck_tile::pk_int4_t,
+                                                        ck_tile::bf8_t,
+                                                        float,
+                                                        ck_tile::bf8_t>{});
+
+        if(quant_mode == "aquant")
+        {
+            return run_gemm_example_prec_type<GemmConfig<ck_tile::pk_int4_t>,
+                                              TypeConfig,
+                                              128,
+                                              ck_tile::QuantType::AQuantGrouped>(
+                a_layout, b_layout, argc, argv);
+        }
+        else
+        {
+            throw std::runtime_error(
+                "Unsupported quantization mode for this datatype! Use 'aquant'.");
+        }
+    }
+    else if(data_type == "i4f32fp8")
+    {
+        using TypeConfig =
+            decltype(GemmQuantTypeConfig<ck_tile::pk_int4_t, ck_tile::fp8_t, float, float>{});
+
+        if(quant_mode == "aquant")
+        {
+            return run_gemm_example_prec_type<GemmConfig<ck_tile::pk_int4_t>,
+                                              TypeConfig,
+                                              128,
+                                              ck_tile::QuantType::AQuantGrouped>(
+                a_layout, b_layout, argc, argv);
+        }
+        else
+        {
+            throw std::runtime_error(
+                "Unsupported quantization mode for this datatype! Use 'aquant'.");
+        }
+    }
+    else if(data_type == "i4f32bf8")
+    {
+        using TypeConfig =
+            decltype(GemmQuantTypeConfig<ck_tile::pk_int4_t, ck_tile::bf8_t, float, float>{});
+
+        if(quant_mode == "aquant")
+        {
+            return run_gemm_example_prec_type<GemmConfig<ck_tile::pk_int4_t>,
+                                              TypeConfig,
+                                              128,
+                                              ck_tile::QuantType::AQuantGrouped>(
+                a_layout, b_layout, argc, argv);
+        }
+        else
+        {
+            throw std::runtime_error(
+                "Unsupported quantization mode for this datatype! Use 'aquant'.");
+        }
+    }
+    else
+    {
+        throw std::runtime_error("Unsupported data type for this operation !!!");
+    }
+}
+
+int main(int argc, char* argv[]) { return !run_gemm_example<GemmConfigQuant>(argc, argv); }
diff --git a/example/ck_tile/38_block_scale_gemm/gemm_utils.hpp b/example/ck_tile/38_block_scale_gemm/gemm_utils.hpp
index 930cdefb7e..555631eaa3 100644
--- a/example/ck_tile/38_block_scale_gemm/gemm_utils.hpp
+++ b/example/ck_tile/38_block_scale_gemm/gemm_utils.hpp
@@ -83,14 +83,13 @@ struct GemmConfigBase
 
     static constexpr ck_tile::index_t TileParitionerGroupNum = 8;
     static constexpr ck_tile::index_t TileParitionerM01      = 4;
-    static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Intrawave;
 
     static constexpr bool PreshuffleQuant  = false;
     static constexpr bool DoubleSmemBuffer = false;
 };
 
 template <typename PrecType>
-struct GemmConfigDecode : public GemmConfigBase
+struct GemmConfigQuant : public GemmConfigBase
 {
     static constexpr ck_tile::index_t M_Tile = 16;
     static constexpr ck_tile::index_t N_Tile = 64;
@@ -105,29 +104,6 @@ struct GemmConfigDecode : public GemmConfigBase
     static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
 
     static constexpr int kBlockPerCu = 1;
-
-    static constexpr auto Scheduler            = ck_tile::GemmPipelineScheduler::Default;
-    static constexpr ck_tile::index_t Pipeline = CK_TILE_PIPELINE_DECODE;
-};
-
-template <typename PrecType>
-struct GemmConfigPrefill : public GemmConfigBase
-{
-    static constexpr ck_tile::index_t M_Tile = 128;
-    static constexpr ck_tile::index_t N_Tile = 128;
-    static constexpr ck_tile::index_t K_Tile = 128 / sizeof(PrecType);
-
-    static constexpr ck_tile::index_t M_Warp = 1;
-    static constexpr ck_tile::index_t N_Warp = 4;
-    static constexpr ck_tile::index_t K_Warp = 1;
-
-    static constexpr ck_tile::index_t M_Warp_Tile = 16;
-    static constexpr ck_tile::index_t N_Warp_Tile = 16;
-    static constexpr ck_tile::index_t K_Warp_Tile = get_k_warp_tile<PrecType, M_Warp_Tile>();
-
-    static constexpr int kBlockPerCu           = 2;
-    static constexpr auto Scheduler            = ck_tile::GemmPipelineScheduler::Default;
-    static constexpr ck_tile::index_t Pipeline = CK_TILE_PIPELINE_PREFILL;
 };
 
 template <typename PrecType>
@@ -148,9 +124,7 @@ struct GemmConfigPreshuffleQuant : public GemmConfigBase
 
     static constexpr int kBlockPerCu = 1;
 
-    static constexpr auto Scheduler            = ck_tile::GemmPipelineScheduler::Default;
-    static constexpr ck_tile::index_t Pipeline = CK_TILE_PIPELINE_PRESHUFFLEQUANT;
-    static constexpr bool PreshuffleQuant      = true;
+    static constexpr bool PreshuffleQuant = true;
 };
 
 template <typename ADataType_,
@@ -237,19 +211,20 @@ auto create_args(int argc, char* argv[])
         .insert("stride_q", "0", "Tensor AQ stride")
         .insert("stride_b", "0", "Tensor B stride")
         .insert("stride_c", "0", "Tensor C stride")
-        .insert("v", "2", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
-        .insert("prec", "i4fp8", "data type. fp8/bf8/i4fp8/i4bf8/i4f32fp8/i4f32bf8")
+        .insert("v", "1", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
+        .insert("prec", "fp8", "data type. fp8/bf8/i4fp8/i4bf8/i4f32fp8/i4f32bf8")
         .insert("warmup", "50", "number of iterations before benchmark the kernel")
         .insert("repeat", "1000", "number of iterations to benchmark the kernel")
         .insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
         .insert("split_k", "1", "splitK value")
         .insert("init", "0", "0:random, 1:linear, 2:constant(1)")
         .insert("persistent", "0", "0:non-persistent, 1:persistent")
-        .insert("as_br_cr", "false", "Choose between as_br_cr and as_bs_cr");
+        .insert("as_br_cr", "false", "Choose between as_br_cr and as_bs_cr")
+        .insert("quant_mode", "aquant", "Choose aquant (default) or rowcol");
 
     bool result = arg_parser.parse(argc, argv);
     return std::make_tuple(result, arg_parser);
 }
 
 // host API
-float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::stream_config& s);
+float gemm_calc_aquant(const ck_tile::QuantGemmHostArgs& args, const ck_tile::stream_config& s);
diff --git a/example/ck_tile/38_block_scale_gemm/run_gemm_aquant_example.inc b/example/ck_tile/38_block_scale_gemm/run_gemm_aquant_example.inc
index 8cf77cb011..5f1d528b2b 100644
--- a/example/ck_tile/38_block_scale_gemm/run_gemm_aquant_example.inc
+++ b/example/ck_tile/38_block_scale_gemm/run_gemm_aquant_example.inc
@@ -3,7 +3,6 @@
 // Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
-#include <bit>
 #include <random>
 #include <stdexcept>
 
@@ -59,7 +58,7 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
                   int n_warmup,
                   int n_repeat)
 {
-    ck_tile::AQuantGemmHostArgs args;
+    ck_tile::QuantGemmHostArgs args;
     args.a_ptr     = a_m_k_dev_buf.GetDeviceBuffer();
     args.aq_ptr    = aq_m_aqk_dev_buf.GetDeviceBuffer();
     args.b_ptr     = b_k_n_dev_buf.GetDeviceBuffer();
@@ -68,7 +67,7 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
     args.M         = M;
     args.N         = N;
     args.K         = K;
-    args.QK        = AQK;
+    args.QK_A      = AQK;
     args.stride_A  = stride_A;
     args.stride_B  = stride_B;
     args.stride_C  = stride_C;
diff --git a/example/ck_tile/38_block_scale_gemm/run_gemm_bquant_example.inc b/example/ck_tile/38_block_scale_gemm/run_gemm_bquant_example.inc
index e3e11bb0a9..4500e2e874 100644
--- a/example/ck_tile/38_block_scale_gemm/run_gemm_bquant_example.inc
+++ b/example/ck_tile/38_block_scale_gemm/run_gemm_bquant_example.inc
@@ -2,7 +2,6 @@
 // Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
-#include <bit>
 #include <random>
 
 template <typename Layout>
@@ -60,7 +59,7 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
                   int n_warmup,
                   int n_repeat)
 {
-    ck_tile::BQuantGemmHostArgs args;
+    ck_tile::QuantGemmHostArgs args;
     args.a_ptr     = a_m_k_dev_buf.GetDeviceBuffer();
     args.b_ptr     = b_k_n_dev_buf.GetDeviceBuffer();
     args.bq_ptr    = bq_bqk_n_dev_buf.GetDeviceBuffer();
@@ -69,7 +68,7 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
     args.M         = M;
     args.N         = N;
     args.K         = K;
-    args.QK        = BQK;
+    args.QK_B      = BQK;
     args.stride_A  = stride_A;
     args.stride_B  = stride_B;
     args.stride_C  = stride_C;
diff --git a/example/ck_tile/38_block_scale_gemm/run_gemm_quant_example.inc b/example/ck_tile/38_block_scale_gemm/run_gemm_quant_example.inc
new file mode 100644
index 0000000000..28ae401717
--- /dev/null
+++ b/example/ck_tile/38_block_scale_gemm/run_gemm_quant_example.inc
@@ -0,0 +1,404 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+#include <random>
+#include <stdexcept>
+
+template <typename Layout>
+static constexpr inline auto is_row_major(Layout layout_)
+{
+    return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
+                                                 ck_tile::tensor_layout::gemm::RowMajor>>{};
+}
+
+template <typename T>
+auto shuffle_aq(const ck_tile::HostTensor<T>& t, int block_aq_k)
+{
+    if(t.get_lengths().size() != 2)
+    {
+        throw std::runtime_error("Host tensor is not rank 2 tensor.");
+    }
+    int m_   = t.get_lengths()[0];
+    int aqk_ = t.get_lengths()[1];
+    if(aqk_ % block_aq_k != 0)
+    {
+        throw std::runtime_error("shuffle_aq needs a aqk of multiple times of block_aq_k.");
+    }
+    ck_tile::HostTensor<T> t_view({m_, aqk_ / block_aq_k, block_aq_k});
+    std::copy(t.begin(), t.end(), t_view.begin());
+    return ck_tile::reference_permute(t_view, {1, 0, 2});
+}
+
+template <typename GemmConfig,
+          typename TypeConfig,
+          typename ALayout,
+          typename AQLayout,
+          typename BLayout,
+          typename BQLayout,
+          typename CLayout,
+          uint32_t QuantGroupSize,
+          ck_tile::QuantType QuantMode>
+float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
+                  ck_tile::DeviceMem& aq_dev_buf,
+                  ck_tile::DeviceMem& b_k_n_dev_buf,
+                  ck_tile::DeviceMem& c_m_n_dev_buf,
+                  ck_tile::DeviceMem* bq_dev_buf,
+                  ck_tile::index_t M,
+                  ck_tile::index_t N,
+                  ck_tile::index_t K,
+                  ck_tile::index_t AQK,
+                  ck_tile::index_t BQK,
+                  ck_tile::index_t stride_A,
+                  ck_tile::index_t stride_AQ,
+                  ck_tile::index_t stride_B,
+                  ck_tile::index_t stride_BQ,
+                  ck_tile::index_t stride_C,
+                  ck_tile::index_t kbatch,
+                  int n_warmup,
+                  int n_repeat)
+{
+    ck_tile::QuantGemmHostArgs args;
+    args.a_ptr     = a_m_k_dev_buf.GetDeviceBuffer();
+    args.aq_ptr    = aq_dev_buf.GetDeviceBuffer();
+    args.b_ptr     = b_k_n_dev_buf.GetDeviceBuffer();
+    args.bq_ptr    = (bq_dev_buf != nullptr) ? bq_dev_buf->GetDeviceBuffer() : nullptr;
+    args.c_ptr     = c_m_n_dev_buf.GetDeviceBuffer();
+    args.k_batch   = kbatch;
+    args.M         = M;
+    args.N         = N;
+    args.K         = K;
+    args.QK_A      = AQK;
+    args.QK_B      = BQK;
+    args.stride_A  = stride_A;
+    args.stride_B  = stride_B;
+    args.stride_C  = stride_C;
+    args.stride_AQ = stride_AQ;
+    args.stride_BQ = stride_BQ;
+
+    float ave_time = gemm_calc_quant<GemmConfig,
+                                     TypeConfig,
+                                     ALayout,
+                                     BLayout,
+                                     CLayout,
+                                     QuantGroupSize,
+                                     QuantMode>(
+        args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat});
+
+    std::size_t flop     = std::size_t(2) * M * N * K;
+    std::size_t num_byte = sizeof(typename TypeConfig::ADataType) * M * K +
+                           sizeof(typename TypeConfig::QDataType) * M * AQK +
+                           sizeof(typename TypeConfig::BDataType) * N * K +
+                           sizeof(typename TypeConfig::CDataType) * M * N;
+    if(bq_dev_buf != nullptr)
+    {
+        num_byte += sizeof(typename TypeConfig::QDataType) * BQK;
+    }
+
+    float tflops     = static_cast<float>(flop) / 1.E9 / ave_time;
+    float gb_per_sec = num_byte / 1.E6 / ave_time;
+
+    std::cout << "Run Gemm kernel with M =" << M << " N =" << N << " K =" << K
+              << " StrideA =" << stride_A << " StrideAQ =" << stride_AQ << " StrideB =" << stride_B
+              << " StrideC =" << stride_C << " A_Layout =" << ALayout::name
+              << " B_Layout =" << BLayout::name << " C_Layout =" << CLayout::name;
+    if constexpr(QuantMode == ck_tile::QuantType::RowColQuant)
+    {
+        std::cout << " StrideBQ =" << stride_BQ;
+    }
+    std::cout << " A_Type = " << DataTypeTraits<typename TypeConfig::ADataType>::name
+              << " AQ_Type = " << DataTypeTraits<typename TypeConfig::QDataType>::name
+              << " B_Type = " << DataTypeTraits<typename TypeConfig::BDataType>::name;
+    if constexpr(!std::is_same_v<typename TypeConfig::QDataType, void>)
+    {
+        std::cout << " BQ_Type = " << DataTypeTraits<typename TypeConfig::QDataType>::name;
+    }
+    std::cout << " Acc_Type = " << DataTypeTraits<typename TypeConfig::AccDataType>::name
+              << " C_Type = " << DataTypeTraits<typename TypeConfig::CDataType>::name
+              << " QuantMode = "
+              << (QuantMode == ck_tile::QuantType::AQuantGrouped ? "AQuantGrouped" : "RowColQuant")
+              << " : " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
+              << std::endl;
+
+    return ave_time;
+}
+
+template <typename GemmConfig,
+          typename TypeConfig,
+          uint32_t QuantGroupSize,
+          ck_tile::QuantType QuantMode,
+          typename ALayout,
+          typename AQLayout,
+          typename BLayout,
+          typename BQLayout,
+          typename CLayout>
+int run_gemm_example_with_layouts(int argc,
+                                  char* argv[],
+                                  const ALayout a_layout                  = ALayout{},
+                                  const AQLayout aq_layout                = AQLayout{},
+                                  const BLayout b_layout                  = BLayout{},
+                                  const BQLayout bq_layout                = BQLayout{},
+                                  [[maybe_unused]] const CLayout c_layout = CLayout{})
+{
+    auto [result, arg_parser] = create_args(argc, argv);
+    if(!result)
+        return -1;
+
+    using ADataType   = typename TypeConfig::ADataType;
+    using AQDataType  = typename TypeConfig::QDataType;
+    using BDataType   = typename TypeConfig::BDataType;
+    using BQDataType  = typename TypeConfig::QDataType;
+    using AccDataType = typename TypeConfig::AccDataType;
+    using CDataType   = typename TypeConfig::CDataType;
+
+    ck_tile::index_t M = arg_parser.get_int("m");
+    ck_tile::index_t N = arg_parser.get_int("n");
+    ck_tile::index_t K = arg_parser.get_int("k");
+
+    if constexpr(QuantMode == ck_tile::QuantType::AQuantGrouped)
+    {
+        if(K % QuantGroupSize != 0)
+        {
+            throw std::runtime_error(
+                "K must be aligned with QuantGroupSize for AQuantGrouped mode");
+        }
+    }
+
+    ck_tile::index_t AQK, BQK;
+    if constexpr(QuantMode == ck_tile::QuantType::AQuantGrouped)
+    {
+        AQK = K / QuantGroupSize; // Group quantization: AQK = K / GroupSize
+        BQK = 0;                  // No B quantization
+    }
+    else if constexpr(QuantMode == ck_tile::QuantType::RowColQuant)
+    {
+        AQK = 1; // Row quantization: tensor shape [M, 1]
+        BQK = N; // Column quantization: tensor shape [1, N]
+    }
+    else
+    {
+        static_assert(false, "Unsupported QuantMode");
+    }
+
+    ck_tile::index_t stride_A  = arg_parser.get_int("stride_a");
+    ck_tile::index_t stride_AQ = arg_parser.get_int("stride_q");
+    ck_tile::index_t stride_B  = arg_parser.get_int("stride_b");
+    ck_tile::index_t stride_C  = arg_parser.get_int("stride_c");
+
+    ck_tile::index_t stride_BQ = 0;
+
+    ck_tile::index_t kbatch      = arg_parser.get_int("split_k");
+    int n_warmup                 = arg_parser.get_int("warmup");
+    int n_repeat                 = arg_parser.get_int("repeat");
+    ck_tile::index_t init_method = arg_parser.get_int("init");
+
+    stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
+    stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
+    stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
+
+    // Conditional stride calculation based on QuantMode
+    if constexpr(QuantMode == ck_tile::QuantType::AQuantGrouped)
+    {
+        stride_AQ = ck_tile::get_default_stride(M, AQK, stride_AQ, is_row_major(aq_layout));
+    }
+    else if constexpr(QuantMode == ck_tile::QuantType::RowColQuant)
+    {
+        stride_AQ = ck_tile::get_default_stride(M, 1, stride_AQ, is_row_major(aq_layout));
+        stride_BQ = ck_tile::get_default_stride(1, N, stride_BQ, is_row_major(bq_layout));
+    }
+
+    ck_tile::HostTensor<ADataType> a_m_k(
+        ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
+    ck_tile::HostTensor<BDataType> b_k_n(
+        ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
+    ck_tile::HostTensor<CDataType> c_m_n_dev_result(
+        ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
+
+    // Create AQ tensor with appropriate shape
+    ck_tile::HostTensor<AQDataType> aq_tensor = [&]() {
+        if constexpr(QuantMode == ck_tile::QuantType::AQuantGrouped)
+        {
+            return ck_tile::HostTensor<AQDataType>(
+                ck_tile::host_tensor_descriptor(M, AQK, stride_AQ, is_row_major(aq_layout)));
+        }
+        else
+        {
+            return ck_tile::HostTensor<AQDataType>(
+                ck_tile::host_tensor_descriptor(M, AQK, stride_AQ, is_row_major(aq_layout)));
+        }
+    }();
+    // Create BQ tensor only for RowColQuant mode
+    std::unique_ptr<ck_tile::HostTensor<BQDataType>> bq_tensor_ptr = nullptr;
+    if constexpr(QuantMode == ck_tile::QuantType::RowColQuant)
+    {
+        bq_tensor_ptr = std::make_unique<ck_tile::HostTensor<BQDataType>>(
+            ck_tile::host_tensor_descriptor(1, N, stride_BQ, is_row_major(bq_layout)));
+    }
+
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_int_distribution<std::uint32_t> fill_seed(0, 500);
+
+    if(init_method == 0)
+    {
+        if constexpr(std::is_same_v<ADataType, ck_tile::pk_int4_t>)
+        {
+            ck_tile::FillUniformDistribution<ck_tile::pk_int4_t>{-5.0f, 5.0f, fill_seed(gen)}(
+                a_m_k);
+        }
+        else
+        {
+            ck_tile::FillUniformDistribution<ADataType>{-2.0f, 3.0f, fill_seed(gen)}(a_m_k);
+        }
+        ck_tile::FillUniformDistribution<AQDataType>{-2.0f, 2.0f, fill_seed(gen)}(aq_tensor);
+        ck_tile::FillUniformDistribution<BDataType>{-5.0f, 5.0f, fill_seed(gen)}(b_k_n);
+
+        if constexpr(QuantMode == ck_tile::QuantType::RowColQuant)
+        {
+            ck_tile::FillUniformDistribution<BQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
+                *bq_tensor_ptr);
+        }
+    }
+    else if(init_method == 1)
+    {
+        std::cout << "Monotonic initialization is not supported." << std::endl;
+        return 0;
+    }
+    else if(init_method == 2)
+    {
+        ck_tile::FillConstant<ADataType>{static_cast<ADataType>(0x22)}(a_m_k);
+        ck_tile::FillConstant<AQDataType>{static_cast<AQDataType>(0.5f)}(aq_tensor);
+        ck_tile::FillConstant<BDataType>{static_cast<BDataType>(0x38)}(b_k_n);
+        if constexpr(QuantMode == ck_tile::QuantType::RowColQuant)
+        {
+            ck_tile::FillConstant<BQDataType>{static_cast<BQDataType>(0.5f)}(*bq_tensor_ptr);
+        }
+    }
+    else
+    {
+        a_m_k.SetZero();
+        aq_tensor.SetZero();
+        b_k_n.SetZero();
+        if constexpr(QuantMode == ck_tile::QuantType::RowColQuant)
+        {
+            bq_tensor_ptr->SetZero();
+        }
+    }
+
+    ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem aq_dev_buf(aq_tensor.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());
+
+    std::unique_ptr<ck_tile::DeviceMem> bq_dev_buf_ptr = nullptr;
+    if constexpr(QuantMode == ck_tile::QuantType::RowColQuant)
+    {
+        bq_dev_buf_ptr =
+            std::make_unique<ck_tile::DeviceMem>(bq_tensor_ptr->get_element_space_size_in_bytes());
+    }
+
+    if constexpr(GemmConfig::PreshuffleQuant && QuantMode == ck_tile::QuantType::AQuantGrouped)
+    {
+        ck_tile::HostTensor<AQDataType> aq_shuffle_host =
+            shuffle_aq(aq_tensor, GemmConfig::K_Tile / QuantGroupSize);
+        aq_dev_buf.ToDevice(aq_shuffle_host.data());
+    }
+    else
+    {
+        aq_dev_buf.ToDevice(aq_tensor.data());
+    }
+
+    a_m_k_dev_buf.ToDevice(a_m_k.data());
+    b_k_n_dev_buf.ToDevice(b_k_n.data());
+    c_m_n_dev_buf.SetZero();
+    c_m_n_dev_result.SetZero();
+
+    if constexpr(QuantMode == ck_tile::QuantType::RowColQuant)
+    {
+        bq_dev_buf_ptr->ToDevice(bq_tensor_ptr->data());
+    }
+
+    invoke_gemm<GemmConfig,
+                TypeConfig,
+                ALayout,
+                AQLayout,
+                BLayout,
+                BQLayout,
+                CLayout,
+                QuantGroupSize,
+                QuantMode>(a_m_k_dev_buf,
+                           aq_dev_buf,
+                           b_k_n_dev_buf,
+                           c_m_n_dev_buf,
+                           (QuantMode == ck_tile::QuantType::RowColQuant) ? bq_dev_buf_ptr.get()
+                                                                          : nullptr,
+                           M,
+                           N,
+                           K,
+                           AQK,
+                           BQK,
+                           stride_A,
+                           stride_AQ,
+                           stride_B,
+                           stride_BQ,
+                           stride_C,
+                           kbatch,
+                           n_warmup,
+                           n_repeat);
+
+    c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
+    bool pass = true;
+
+    if(arg_parser.get_int("v") == 1)
+    {
+        ck_tile::HostTensor<CDataType> c_m_n_host_ref(
+            ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
+        c_m_n_host_ref.SetZero();
+
+        if constexpr(QuantMode == ck_tile::QuantType::AQuantGrouped)
+        {
+            ck_tile::reference_gemm_quant<ADataType,
+                                          AQDataType,
+                                          BDataType,
+                                          AccDataType,
+                                          CDataType,
+                                          QuantGroupSize,
+                                          true>(a_m_k, aq_tensor, b_k_n, c_m_n_host_ref);
+        }
+        else
+        {
+            ck_tile::reference_gemm_rowcol_quant<ADataType,
+                                                 AQDataType,
+                                                 BDataType,
+                                                 BQDataType,
+                                                 AccDataType,
+                                                 CDataType>(
+                a_m_k, aq_tensor, b_k_n, *bq_tensor_ptr, c_m_n_host_ref);
+        }
+
+        const float max_accumulated_value =
+            *std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
+        const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
+            K, kbatch, max_accumulated_value);
+        pass = ck_tile::check_err(c_m_n_dev_result,
+                                  c_m_n_host_ref,
+                                  "Error: Incorrect results!",
+                                  rtol_atol.at(ck_tile::number<0>{}),
+                                  rtol_atol.at(ck_tile::number<1>{}));
+
+        if(!pass)
+        {
+            std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
+                      << " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
+                      << std::endl;
+        }
+        std::cout << "CPU verification " << (pass ? "Passed!" : "Failed ...") << std::endl;
+    }
+    else if(arg_parser.get_int("v") == 2)
+    {
+        std::cout << "GPU verification is not implemented yet. Re-run with -v=1" << std::endl;
+        return false;
+    }
+
+    return pass;
+}
diff --git a/include/ck_tile/host/reference/reference_gemm.hpp b/include/ck_tile/host/reference/reference_gemm.hpp
index 70ca44170e..5a26cfbcd2 100644
--- a/include/ck_tile/host/reference/reference_gemm.hpp
+++ b/include/ck_tile/host/reference/reference_gemm.hpp
@@ -115,6 +115,92 @@ CK_TILE_HOST void reference_gemm_quant(const HostTensor<ADataType>& a_m_k,
     std::cout << std::endl;
 }
 
+template <typename ADataType,
+          typename AQDataType,
+          typename BDataType,
+          typename BQDataType,
+          typename AccDataType,
+          typename CDataType,
+          typename AElementOp   = ck_tile::identity,
+          typename BElementOp   = ck_tile::identity,
+          typename ACCElementOp = ck_tile::identity>
+CK_TILE_HOST void reference_gemm_rowcol_quant(const HostTensor<ADataType>& a_m_k,
+                                              const HostTensor<AQDataType>& aq_m_1,
+                                              const HostTensor<BDataType>& b_k_n,
+                                              const HostTensor<BQDataType>& bq_1_n,
+                                              HostTensor<CDataType>& c_m_n,
+                                              const AElementOp& a_element_op     = {},
+                                              const BElementOp& b_element_op     = {},
+                                              const ACCElementOp& acc_element_op = {})
+{
+    static_assert(std::is_same_v<ADataType, fp8_t> || std::is_same_v<ADataType, bf8_t>);
+    static_assert(std::is_same_v<BDataType, fp8_t> || std::is_same_v<BDataType, bf8_t>);
+    static_assert(std::is_same_v<AccDataType, float>);
+    static_assert(std::is_same_v<CDataType, float> || std::is_same_v<CDataType, ck_tile::half_t>);
+    static_assert(std::is_same_v<AQDataType, float> && std::is_same_v<BQDataType, float>);
+    const std::size_t M = a_m_k.get_length(0);
+    const std::size_t N = b_k_n.get_length(1);
+    const std::size_t K = a_m_k.get_length(1);
+
+    auto f_mn = [&](auto m, auto n) {
+        // Init accumulator
+        AccDataType v_acc = 0;
+        // Get row scale for A and column scale for B
+        float a_scale = aq_m_1(m, 0);
+        float b_scale = bq_1_n(0, n);
+
+        // Compute the dot product
+        for(std::size_t k = 0; k < K; ++k)
+        {
+            AccDataType v_a;
+            AccDataType v_b;
+
+            // Process A data
+            if constexpr(std::is_same_v<ADataType, pk_int4_t>)
+            {
+                const pk_int4_t pk_val  = a_element_op(a_m_k(m, k));
+                const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t_signed_conversion(pk_val);
+                if(k % 2 == 1)
+                    v_a = fp32_val.hi;
+                else
+                    v_a = fp32_val.lo;
+            }
+            else
+            {
+                v_a = ck_tile::type_convert<AccDataType>(a_element_op(a_m_k(m, k)));
+            }
+
+            // Process B data
+            if constexpr(std::is_same_v<BDataType, pk_int4_t>)
+            {
+                const pk_int4_t pk_val  = b_element_op(b_k_n(k, n));
+                const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t_signed_conversion(pk_val);
+                if(k % 2 == 1)
+                    v_b = fp32_val.hi;
+                else
+                    v_b = fp32_val.lo;
+            }
+            else if constexpr(std::is_same_v<BDataType, fp8_t>)
+            {
+                v_b = fp8_to_float_raw(b_element_op(b_k_n(k, n)));
+            }
+            else
+            {
+                v_b = ck_tile::type_convert<AccDataType>(b_element_op(b_k_n(k, n)));
+            }
+
+            v_acc += v_a * v_b;
+        }
+
+        v_acc = v_acc * a_scale * b_scale;
+
+        c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
+    };
+
+    make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
+    std::cout << std::endl;
+}
+
 template <typename ADataType,
           typename BDataType,
           typename AccDataType,
diff --git a/include/ck_tile/ops/epilogue/cshuffle_epilogue.hpp b/include/ck_tile/ops/epilogue/cshuffle_epilogue.hpp
index 7510df091c..c6cfe84ac5 100644
--- a/include/ck_tile/ops/epilogue/cshuffle_epilogue.hpp
+++ b/include/ck_tile/ops/epilogue/cshuffle_epilogue.hpp
@@ -6,6 +6,9 @@
 #include "ck_tile/core.hpp"
 #include "ck_tile/ops/gemm/warp/warp_gemm_dispatcher.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
+#include "ck_tile/ops/elementwise/unary_element_wise_operation.hpp"
+
+#include <optional>
 
 namespace ck_tile {
 
@@ -210,8 +213,12 @@ struct CShuffleEpilogue
                                   KPerXdl,
                                   isCTransposed>;
 
-    using CWarpDstr   = typename WG::CWarpDstr;
-    using CWarpTensor = typename WG::CWarpTensor;
+    using CWarpDstr         = typename WG::CWarpDstr;
+    using CWarpTensor       = typename WG::CWarpTensor;
+    using CWarpDstrEncoding = typename WG::CWarpDstrEncoding;
+    using SFC               = space_filling_curve<sequence<kMPerBlock, kNPerBlock>,
+                                                  sequence<0, 1>,
+                                                  sequence<MPerIterationShuffle, NPerIterationShuffle>>;
 
     template <typename Problem>
     CK_TILE_HOST_DEVICE static constexpr auto MakeLdsBlockDescriptor()
@@ -257,11 +264,120 @@ struct CShuffleEpilogue
         return MPerIterationShuffle * NPerIterationShuffle * sizeof(ODataType);
     }
 
-    template <typename ODramWindow, typename OAccTile, typename DsDramWindows>
+    template <auto iAccess, typename LdsTile, typename ScaleM, typename ScaleN>
+    CK_TILE_DEVICE void
+    scale_tile(LdsTile& lds_tile, ScaleM& scale_m_window, ScaleN& scale_n_window)
+    {
+        // Load tiles
+        const auto scale_m_tile = load_tile(scale_m_window);
+        const auto scale_n_tile = load_tile(scale_n_window);
+
+        // Compute element-wise product in-place i.e. lds_tile = lds_tile * scale_m * scale_n
+        tile_elementwise_inout(
+            element_wise::MultiDMultiply{}, lds_tile, lds_tile, scale_m_tile, scale_n_tile);
+
+        // Move scale windows
+        constexpr index_t num_access = SFC::get_num_of_access();
+        if constexpr(iAccess != num_access - 1)
+        {
+            constexpr auto step = SFC::get_forward_step(iAccess);
+
+            move_tile_window(scale_m_window, {step.at(number<0>{}), step.at(number<1>{})});
+            move_tile_window(scale_n_window, {step.at(number<0>{}), step.at(number<1>{})});
+        }
+    }
+
+    template <auto iAccess, typename OAccTile, typename LdsTile>
+    CK_TILE_DEVICE void slice_acc_tile(const OAccTile& o_acc_tile, LdsTile& lds_tile)
+    {
+        constexpr auto idx_y_start = SFC::get_index(iAccess);
+
+        constexpr auto mIter = number<idx_y_start.at(number<0>{}) / (MPerIterationShuffle)>{};
+        constexpr auto nIter = number<idx_y_start.at(number<1>{}) / (NPerIterationShuffle)>{};
+        constexpr auto c_warp_y_lengths =
+            to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
+        constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
+
+        lds_tile.get_thread_buffer() = o_acc_tile.get_y_sliced_thread_data(
+            merge_sequences(
+                sequence<mIter * NumMXdlPerWavePerShuffle, nIter * NumNXdlPerWavePerShuffle>{},
+                c_warp_y_index_zeros),
+            merge_sequences(sequence<NumMXdlPerWavePerShuffle, NumNXdlPerWavePerShuffle>{},
+                            c_warp_y_lengths));
+    }
+
+    template <typename LdsTile, typename InLdsWindow>
+    CK_TILE_DEVICE void cast_lds_tile(LdsTile& lds_tile, InLdsWindow& in_lds_window)
+    {
+        const auto c_warptile_in_tensor_casted = cast_tile<ODataType>(lds_tile);
+
+        store_tile(in_lds_window, c_warptile_in_tensor_casted);
+    }
+
+    template <typename DramWindows, typename COutTensor>
+    CK_TILE_DEVICE void apply_d_tensors(DramWindows& d_dram_windows, COutTensor& c_out_tensor)
+    {
+        const auto ds_tensor = generate_tuple(
+            [&](auto idx) { return load_tile(d_dram_windows[idx]); }, number<NumDTensor>{});
+
+        const auto c_ds_tiles = concat_tuple_of_reference(
+            tie(c_out_tensor, c_out_tensor),
+            generate_tie([&](auto idx) -> const auto& { return ds_tensor[idx]; },
+                         number<NumDTensor>{}));
+
+        tile_elementwise_inout_unpack(typename Problem::CDElementwise{}, c_ds_tiles);
+    }
+
+    template <typename OutDramWindow, typename COutTensor>
+    CK_TILE_DEVICE void store_to_dram(OutDramWindow& out_dram_window,
+                                      const COutTensor& c_out_tensor)
+    {
+        if constexpr(MemoryOperation == memory_operation_enum::set)
+        {
+            store_tile(out_dram_window, c_out_tensor);
+        }
+        else
+        {
+            update_tile(out_dram_window, c_out_tensor);
+        }
+    }
+
+    /**
+     * @brief Move both the output and D tensors windows for the next access.
+     */
+    template <auto iAccess, typename OutDramWindow, typename DDramWindows>
+    CK_TILE_DEVICE void move_windows(OutDramWindow& out_dram_window, DDramWindows& d_dram_windows)
+    {
+        constexpr index_t num_access = SFC::get_num_of_access();
+        if constexpr(iAccess != num_access - 1)
+        {
+            constexpr auto step = SFC::get_forward_step(iAccess);
+
+            // move the output dram window
+            move_tile_window(out_dram_window, {step.at(number<0>{}), step.at(number<1>{})});
+
+            // move windows for each of the D matrices (inputs for element-wise)
+            static_for<0, NumDTensor, 1>{}([&](auto idx) {
+                move_tile_window(d_dram_windows[idx], {step.at(number<0>{}), step.at(number<1>{})});
+            });
+        }
+    }
+
+    // TODO: Check if there would be nicer ways to overload rather than with EmptyScale or nullptr_t
+    struct EmptyScale
+    {
+    };
+    template <typename ODramWindow,
+              typename OAccTile,
+              typename DsDramWindows,
+              typename ScaleM = EmptyScale,
+              typename ScaleN = EmptyScale>
     CK_TILE_DEVICE auto operator()(ODramWindow& out_dram_window,
                                    const OAccTile& o_acc_tile,
                                    const DsDramWindows& ds_dram_windows,
-                                   void* p_smem)
+                                   void* p_smem,
+                                   const ScaleM& scale_m = {},
+                                   const ScaleN& scale_n = {})
     {
         constexpr auto LdsTileDistr = make_static_tile_distribution(MakeLdsDistributionEncode());
 
@@ -282,9 +398,6 @@ struct CShuffleEpilogue
             make_tuple(number<MPerIterationShuffle>{}, number<NPerIterationShuffle>{}),
             {0, 0});
 
-        using SFC                    = space_filling_curve<sequence<kMPerBlock, kNPerBlock>,
-                                                           sequence<0, 1>,
-                                                           sequence<MPerIterationShuffle, NPerIterationShuffle>>;
         constexpr index_t num_access = SFC::get_num_of_access();
 
         static_assert(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>,
@@ -306,60 +419,46 @@ struct CShuffleEpilogue
             },
             number<NumDTensor>{});
 
-        constexpr auto c_warp_y_lengths =
-            to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
-        constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
+        constexpr bool has_scales =
+            !std::is_same<ScaleM, EmptyScale>::value && !std::is_same<ScaleN, EmptyScale>::value;
+        auto scale_m_window = [&]() {
+            if constexpr(has_scales)
+            {
+                return make_tile_window(scale_m, lds_tile.get_tile_distribution());
+            }
+            else
+            {
+                return EmptyScale{};
+            }
+        }();
+        auto scale_n_window = [&]() {
+            if constexpr(has_scales)
+            {
+                return make_tile_window(scale_n, lds_tile.get_tile_distribution());
+            }
+            else
+            {
+                return EmptyScale{};
+            }
+        }();
 
         static_for<0, num_access, 1>{}([&](auto iAccess) {
             block_sync_lds();
-            constexpr auto idx_y_start = SFC::get_index(iAccess);
+            slice_acc_tile<iAccess>(o_acc_tile, lds_tile);
 
-            constexpr auto mIter = number<idx_y_start.at(number<0>{}) / (MPerIterationShuffle)>{};
-            constexpr auto nIter = number<idx_y_start.at(number<1>{}) / (NPerIterationShuffle)>{};
+            if constexpr(has_scales)
+            {
+                scale_tile<iAccess>(lds_tile, scale_m_window, scale_n_window);
+            }
 
-            lds_tile.get_thread_buffer() = o_acc_tile.get_y_sliced_thread_data(
-                merge_sequences(
-                    sequence<mIter * NumMXdlPerWavePerShuffle, nIter * NumNXdlPerWavePerShuffle>{},
-                    c_warp_y_index_zeros),
-                merge_sequences(sequence<NumMXdlPerWavePerShuffle, NumNXdlPerWavePerShuffle>{},
-                                c_warp_y_lengths));
-
-            const auto c_warptile_in_tensor_casted = cast_tile<ODataType>(lds_tile);
-
-            store_tile(in_lds_window, c_warptile_in_tensor_casted);
+            cast_lds_tile(lds_tile, in_lds_window);
             block_sync_lds();
 
             auto c_out_tensor = load_tile(make_tile_window(out_lds_window, dram_tile_distribution));
 
-            const auto ds_tensor = generate_tuple(
-                [&](auto idx) { return load_tile(d_dram_windows[idx]); }, number<NumDTensor>{});
-
-            const auto c_ds_tiles = concat_tuple_of_reference(
-                tie(c_out_tensor, c_out_tensor),
-                generate_tie([&](auto idx) -> const auto& { return ds_tensor[idx]; },
-                             number<NumDTensor>{}));
-
-            tile_elementwise_inout_unpack(typename Problem::CDElementwise{}, c_ds_tiles);
-
-            if constexpr(MemoryOperation == memory_operation_enum::set)
-            {
-                store_tile(out_dram_window, c_out_tensor);
-            }
-            else
-            {
-                update_tile(out_dram_window, c_out_tensor);
-            }
-            if constexpr(iAccess != num_access - 1)
-            {
-                constexpr auto step = SFC::get_forward_step(iAccess);
-
-                move_tile_window(out_dram_window, {step.at(number<0>{}), step.at(number<1>{})});
-
-                static_for<0, NumDTensor, 1>{}([&](auto idx) {
-                    move_tile_window(d_dram_windows[idx],
-                                     {step.at(number<0>{}), step.at(number<1>{})});
-                });
-            }
+            apply_d_tensors(d_dram_windows, c_out_tensor);
+            store_to_dram(out_dram_window, c_out_tensor);
+            move_windows<iAccess>(out_dram_window, d_dram_windows);
         });
     }
 };
diff --git a/include/ck_tile/ops/gemm_group_quant.hpp b/include/ck_tile/ops/gemm_group_quant.hpp
index 92a53dd5ea..7c04741166 100644
--- a/include/ck_tile/ops/gemm_group_quant.hpp
+++ b/include/ck_tile/ops/gemm_group_quant.hpp
@@ -5,8 +5,7 @@
 
 #include "ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_aquant_bs_cr.hpp"
 #include "ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_bs_bquant_cr.hpp"
-#include "ck_tile/ops/gemm_group_quant/kernel/gemm_aquant_kernel.hpp"
-#include "ck_tile/ops/gemm_group_quant/kernel/gemm_bquant_kernel.hpp"
+#include "ck_tile/ops/gemm_group_quant/kernel/gemm_quant_kernel.hpp"
 #include "ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_ag_bg_cr_base.hpp"
 #include "ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_ag_bg_cr_policy.hpp"
 #include "ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_ag_bg_cr_v3.hpp"
diff --git a/include/ck_tile/ops/gemm_group_quant/kernel/gemm_bquant_kernel.hpp b/include/ck_tile/ops/gemm_group_quant/kernel/gemm_bquant_kernel.hpp
deleted file mode 100644
index 24e69d2628..0000000000
--- a/include/ck_tile/ops/gemm_group_quant/kernel/gemm_bquant_kernel.hpp
+++ /dev/null
@@ -1,679 +0,0 @@
-// SPDX-License-Identifier: MIT
-// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
-
-#pragma once
-
-#include <iostream>
-#include <string>
-
-#include "ck_tile/core.hpp"
-#include "ck_tile/ops/common.hpp"
-#include "ck_tile/host/concat.hpp"
-
-namespace ck_tile {
-
-struct BQuantGemmProblem
-{
-    CK_TILE_HOST BQuantGemmProblem() = default;
-    CK_TILE_HOST BQuantGemmProblem(index_t M_,
-                                   index_t N_,
-                                   index_t K_,
-                                   index_t QK_,
-                                   index_t stride_A_,
-                                   index_t stride_B_,
-                                   index_t stride_C_,
-                                   index_t stride_BQ_)
-        : M(M_),
-          N(N_),
-          K(K_),
-          QK(QK_),
-          stride_A(stride_A_),
-          stride_B(stride_B_),
-          stride_C(stride_C_),
-          stride_BQ(stride_BQ_)
-    {
-    }
-
-    index_t M;
-    index_t N;
-    index_t K;
-    index_t QK;
-    index_t stride_A;
-    index_t stride_B;
-    index_t stride_C;
-    index_t stride_BQ;
-};
-
-struct BQuantGemmHostArgs : public BQuantGemmProblem
-{
-    CK_TILE_HOST BQuantGemmHostArgs() = default;
-    CK_TILE_HOST BQuantGemmHostArgs(const void* a_ptr_,
-                                    const void* b_ptr_,
-                                    void* c_ptr_,
-                                    const void* bq_ptr_,
-                                    index_t k_batch_,
-                                    index_t M_,
-                                    index_t N_,
-                                    index_t K_,
-                                    index_t QK_,
-                                    index_t stride_A_,
-                                    index_t stride_B_,
-                                    index_t stride_C_,
-                                    index_t stride_BQ_)
-        : BQuantGemmProblem(M_, N_, K_, QK_, stride_A_, stride_B_, stride_C_, stride_BQ_),
-          a_ptr(a_ptr_),
-          b_ptr(b_ptr_),
-          bq_ptr(bq_ptr_),
-          c_ptr(c_ptr_),
-          k_batch(k_batch_)
-    {
-    }
-
-    const void* a_ptr;
-    const void* b_ptr;
-    const void* bq_ptr;
-    void* c_ptr;
-    index_t k_batch;
-};
-
-struct BQuantGemmKernelArgs
-{
-    const void* a_ptr;
-    const void* b_ptr;
-    const void* bq_ptr;
-    void* c_ptr;
-    index_t M;
-    index_t N;
-    index_t K;
-    index_t QK;
-    index_t stride_A;
-    index_t stride_B;
-    index_t stride_C;
-    index_t stride_BQ;
-    index_t k_batch;
-};
-
-template <typename TilePartitioner_, typename GemmPipeline_, typename EpiloguePipeline_>
-struct BQuantGemmKernel
-{
-    using TilePartitioner               = remove_cvref_t<TilePartitioner_>;
-    using GemmPipeline                  = remove_cvref_t<GemmPipeline_>;
-    using EpiloguePipeline              = remove_cvref_t<EpiloguePipeline_>;
-    using ALayout                       = remove_cvref_t<typename GemmPipeline::ALayout>;
-    using BLayout                       = remove_cvref_t<typename GemmPipeline::BLayout>;
-    using BQLayout                      = remove_cvref_t<typename GemmPipeline::BQLayout>;
-    using CLayout                       = remove_cvref_t<typename GemmPipeline::CLayout>;
-    static constexpr index_t kBlockSize = GemmPipeline::BlockSize;
-
-    using ADataType  = remove_cvref_t<typename GemmPipeline::ADataType>;
-    using BDataType  = remove_cvref_t<typename GemmPipeline::BDataType>;
-    using BQDataType = remove_cvref_t<typename GemmPipeline::BQDataType>;
-    using CDataType  = remove_cvref_t<typename EpiloguePipeline::ODataType>;
-
-    static constexpr auto I0 = number<0>();
-    static constexpr auto I1 = number<1>();
-    static constexpr auto I2 = number<2>();
-    static constexpr auto I3 = number<3>();
-
-    [[nodiscard]] CK_TILE_HOST static const std::string GetName()
-    {
-        // clang-format off
-        return concat('_', "gemm", gemm_prec_str<ADataType, BDataType>, GemmPipeline::GetName());
-        // clang-format on
-    }
-
-    CK_TILE_HOST static constexpr auto GridSize(index_t M, index_t N, index_t KBatch)
-    {
-        return dim3(TilePartitioner::GridSize(M, N), 1, KBatch);
-    }
-
-    CK_TILE_HOST static constexpr auto BlockSize() { return dim3(kBlockSize); }
-
-    CK_TILE_HOST static constexpr BQuantGemmKernelArgs
-    MakeKernelArgs(const BQuantGemmHostArgs& hostArgs)
-    {
-        return BQuantGemmKernelArgs{hostArgs.a_ptr,
-                                    hostArgs.b_ptr,
-                                    hostArgs.bq_ptr,
-                                    hostArgs.c_ptr,
-                                    hostArgs.M,
-                                    hostArgs.N,
-                                    hostArgs.K,
-                                    hostArgs.QK,
-                                    hostArgs.stride_A,
-                                    hostArgs.stride_B,
-                                    hostArgs.stride_C,
-                                    hostArgs.stride_BQ,
-                                    hostArgs.k_batch};
-    }
-
-    CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
-    {
-        return max(GemmPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
-    }
-
-    struct SplitKBatchOffset
-    {
-        __device__ SplitKBatchOffset(const BQuantGemmKernelArgs& kargs,
-                                     const std::size_t k_id = blockIdx.z)
-        {
-            constexpr auto K1   = TilePartitioner::BlockGemmShape::WarpTile::at(number<2>{});
-            const index_t K_t   = __builtin_amdgcn_readfirstlane(kargs.k_batch * K1);
-            const index_t KRead = __builtin_amdgcn_readfirstlane((kargs.K + K_t - 1) / K_t * K1);
-
-            if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
-            {
-                a_k_split_offset = __builtin_amdgcn_readfirstlane(k_id * KRead);
-            }
-            else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
-            {
-                a_k_split_offset = __builtin_amdgcn_readfirstlane(k_id * KRead * kargs.stride_A);
-            }
-
-            if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
-            {
-                b_k_split_offset = __builtin_amdgcn_readfirstlane(k_id * KRead * kargs.stride_B);
-            }
-            else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
-            {
-                b_k_split_offset = __builtin_amdgcn_readfirstlane(k_id * KRead);
-            }
-
-            if(k_id < static_cast<uint32_t>(kargs.k_batch - 1))
-            {
-                splitted_k = __builtin_amdgcn_readfirstlane(KRead);
-            }
-            else
-            {
-                splitted_k = __builtin_amdgcn_readfirstlane(kargs.K - KRead * (kargs.k_batch - 1));
-            }
-        }
-
-        index_t a_k_split_offset;
-        index_t b_k_split_offset;
-        index_t splitted_k;
-    };
-
-    CK_TILE_HOST static bool IsSupportedArgument(const BQuantGemmKernelArgs& kargs)
-    {
-        if(kargs.k_batch != 1)
-        {
-            if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-            {
-                CK_TILE_ERROR("Conditions not met for Kbatch >1 !");
-            }
-            return false;
-        }
-
-        static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
-        if(kargs.QK % GemmPipeline::GetVectorSizeBQ() != 0)
-        {
-            if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-            {
-                CK_TILE_ERROR("K is not a multiple of vector load size for A tensor!");
-            }
-            return false;
-        }
-
-        if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
-        {
-            if(kargs.K % (TilePartitioner::KPerBlock * kargs.k_batch) != 0 &&
-               GemmPipeline::kPadK == false)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR("Can't support K that is not a multiple of k_batch * KPerBlock "
-                                  "without padding!");
-                }
-                return false;
-            }
-            if(kargs.K % GemmPipeline::GetVectorSizeA() != 0)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR("K is not a multiple of vector load size for A tensor!");
-                }
-                return false;
-            }
-        }
-        else
-        {
-            if(kargs.M % TilePartitioner::MPerBlock != 0 && GemmPipeline::kPadM == false)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR(
-                        "Can't support M that is not a multiple of MPerBlock without padding!");
-                }
-                return false;
-            }
-            if(kargs.M % GemmPipeline::GetVectorSizeA() != 0)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR("M is not a multiple of vector load size for A tensor!");
-                }
-                return false;
-            }
-        }
-
-        if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
-        {
-            if(kargs.N % TilePartitioner::NPerBlock != 0 && GemmPipeline::kPadN == false)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR(
-                        "Can't support N that is not a multiple of NPerBlock without padding!");
-                }
-                return false;
-            }
-            if(kargs.N % GemmPipeline::GetVectorSizeB() != 0)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR("N is not a multiple of vector load size for B tensor!");
-                }
-                return false;
-            }
-        }
-        else
-        {
-            if(kargs.K % (TilePartitioner::KPerBlock * kargs.k_batch) != 0 &&
-               GemmPipeline::kPadK == false)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR("Can't support K that is not a multiple of k_batch * KPerBlock "
-                                  "without padding!");
-                }
-                return false;
-            }
-            if(kargs.K % GemmPipeline::GetVectorSizeB() != 0)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR("K is not a multiple of vector load size for B tensor!");
-                }
-                return false;
-            }
-        }
-
-        if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
-        {
-            if(kargs.N % TilePartitioner::NPerBlock != 0 && GemmPipeline::kPadN == false)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR(
-                        "Can't support N that is not a multiple of NPerBlock without padding!");
-                }
-                return false;
-            }
-            if(kargs.N % EpiloguePipeline::GetVectorSizeC() != 0)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR("N is not a multiple of vector load size for C tensor!");
-                }
-                return false;
-            }
-        }
-        else
-        {
-            if(kargs.M % TilePartitioner::MPerBlock != 0 && GemmPipeline::kPadM == false)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR(
-                        "Can't support M that is not a multiple of MPerBlock without padding!");
-                }
-                return false;
-            }
-            if(kargs.M % EpiloguePipeline::GetVectorSizeC() != 0)
-            {
-                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
-                {
-                    CK_TILE_ERROR("M is not a multiple of vector load size for C tensor!");
-                }
-                return false;
-            }
-        }
-        return true;
-    }
-
-    template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
-    CK_TILE_DEVICE static auto MakeGemmTensorViews(const ADataType* a_ptr,
-                                                   const BDataType* b_ptr,
-                                                   const BQDataType* bq_ptr,
-                                                   CDataType* c_ptr,
-                                                   const BQuantGemmKernelArgs& kargs,
-                                                   const SplitKBatchOffset& splitk_batch_offset)
-    {
-        static_assert(!TilePartitioner::BlockGemmShape::PermuteA, "Not implemented!");
-        const auto& a_tensor_view = [&]() {
-            if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
-            {
-                return make_naive_tensor_view<address_space_enum::global>(
-                    a_ptr,
-                    make_tuple(kargs.M, splitk_batch_offset.splitted_k),
-                    make_tuple(kargs.stride_A, 1),
-                    number<GemmPipeline::GetVectorSizeA()>{},
-                    number<1>{});
-            }
-            else
-            {
-                return make_naive_tensor_view<address_space_enum::global>(
-                    a_ptr,
-                    make_tuple(splitk_batch_offset.splitted_k, kargs.M),
-                    make_tuple(kargs.stride_A, 1),
-                    number<GemmPipeline::GetVectorSizeA()>{},
-                    number<1>{});
-            }
-        }();
-
-        const auto& bq_tensor_view = [&]() {
-            static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
-            return make_naive_tensor_view<address_space_enum::global>(
-                bq_ptr,
-                make_tuple(kargs.N, kargs.QK),
-                make_tuple(kargs.stride_BQ, 1),
-                number<GemmPipeline::GetVectorSizeBQ()>{},
-                number<1>{});
-        }();
-
-        const auto& b_tensor_view = [&]() {
-            if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
-            {
-                if constexpr(TilePartitioner::BlockGemmShape::PermuteB)
-                {
-                    constexpr index_t K1          = GemmPipeline::GetSmemPackB();
-                    const index_t K0              = splitk_batch_offset.splitted_k / K1;
-                    constexpr index_t VectorSizeB = std::min(K1, GemmPipeline::GetVectorSizeB());
-                    const auto b_k0_n_k1_desc =
-                        make_naive_tensor_descriptor(make_tuple(K0, kargs.N, K1),
-                                                     make_tuple(kargs.N * K1, K1, I1),
-                                                     number<VectorSizeB>{},
-                                                     number<1>{});
-                    const auto b_n_k_desc = transform_tensor_descriptor(
-                        b_k0_n_k1_desc,
-                        make_tuple(make_merge_transform(make_tuple(K0, K1)),
-                                   make_pass_through_transform(kargs.N)),
-                        make_tuple(sequence<0, 2>{}, sequence<1>{}),
-                        make_tuple(sequence<0>{}, sequence<1>{}));
-                    return make_tensor_view<address_space_enum::global>(b_ptr, b_n_k_desc);
-                }
-                else
-                {
-                    return make_naive_tensor_view<address_space_enum::global>(
-                        b_ptr,
-                        make_tuple(splitk_batch_offset.splitted_k, kargs.N),
-                        make_tuple(kargs.stride_B, 1),
-                        number<GemmPipeline::GetVectorSizeB()>{},
-                        number<1>{});
-                }
-            }
-            else
-            {
-                if constexpr(TilePartitioner::BlockGemmShape::PermuteB)
-                {
-                    constexpr index_t K1          = GemmPipeline::GetSmemPackB();
-                    const index_t K0              = splitk_batch_offset.splitted_k / K1;
-                    constexpr index_t VectorSizeB = std::min(K1, GemmPipeline::GetVectorSizeB());
-                    const auto b_k0_n_k1_desc =
-                        make_naive_tensor_descriptor(make_tuple(K0, kargs.N, K1),
-                                                     make_tuple(kargs.N * K1, K1, I1),
-                                                     number<VectorSizeB>{},
-                                                     number<1>{});
-                    const auto b_n_k_desc = transform_tensor_descriptor(
-                        b_k0_n_k1_desc,
-                        make_tuple(make_merge_transform(make_tuple(K0, K1)),
-                                   make_pass_through_transform(kargs.N)),
-                        make_tuple(sequence<0, 2>{}, sequence<1>{}),
-                        make_tuple(sequence<1>{}, sequence<0>{}));
-                    return make_tensor_view<address_space_enum::global>(b_ptr, b_n_k_desc);
-                }
-                else
-                {
-                    return make_naive_tensor_view<address_space_enum::global>(
-                        b_ptr,
-                        make_tuple(kargs.N, splitk_batch_offset.splitted_k),
-                        make_tuple(kargs.stride_B, 1),
-                        number<GemmPipeline::GetVectorSizeB()>{},
-                        number<1>{});
-                }
-            }
-        }();
-
-        // TODO: enable vector write for C in ColMajor
-        const auto& c_tensor_view = [&]() {
-            if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
-            {
-                return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
-                    c_ptr,
-                    make_tuple(kargs.M, kargs.N),
-                    make_tuple(kargs.stride_C, 1),
-                    number<EpiloguePipeline::GetVectorSizeC()>{},
-                    number<1>{});
-            }
-            else
-            {
-                return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
-                    c_ptr,
-                    make_tuple(kargs.M, kargs.N),
-                    make_tuple(1, kargs.stride_C),
-                    number<1>{},
-                    number<1>{});
-            }
-        }();
-
-        return make_tuple(a_tensor_view, bq_tensor_view, b_tensor_view, c_tensor_view);
-    }
-
-    template <typename TensorView>
-    CK_TILE_DEVICE static auto MakeGemmPadViews(const TensorView& views)
-    {
-        const auto& a_pad_view = [&]() {
-            const auto& a_tensor_view = views.at(I0);
-            if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
-            {
-                return pad_tensor_view(a_tensor_view,
-                                       make_tuple(number<TilePartitioner::MPerBlock>{},
-                                                  number<TilePartitioner::KPerBlock>{}),
-                                       sequence<false, GemmPipeline::kPadK>{});
-            }
-            else
-            {
-                return pad_tensor_view(a_tensor_view,
-                                       make_tuple(number<TilePartitioner::KPerBlock>{},
-                                                  number<TilePartitioner::MPerBlock>{}),
-                                       sequence<false, GemmPipeline::kPadM>{});
-            }
-        }();
-
-        const auto& bq_pad_view = [&]() {
-            const auto& bq_tensor_view = views.at(I1);
-            static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
-            return pad_tensor_view(
-                bq_tensor_view,
-                make_tuple(number<TilePartitioner::NPerBlock>{},
-                           number<TilePartitioner::KPerBlock / GemmPipeline::QuantGroupSize>{}),
-                // TODO: Add support for padding.
-                sequence<false, false>{});
-        }();
-
-        const auto& b_pad_view = [&]() {
-            const auto& b_tensor_view = views.at(I2);
-            if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::ColumnMajor>)
-            {
-                return pad_tensor_view(b_tensor_view,
-                                       make_tuple(number<TilePartitioner::NPerBlock>{},
-                                                  number<TilePartitioner::KPerBlock>{}),
-                                       sequence<false, GemmPipeline::kPadK>{});
-            }
-            else
-            {
-                return pad_tensor_view(b_tensor_view,
-                                       make_tuple(number<TilePartitioner::KPerBlock>{},
-                                                  number<TilePartitioner::NPerBlock>{}),
-                                       sequence<false, GemmPipeline::kPadN>{});
-            }
-        }();
-
-        // TODO vector write in for C in ColMajor
-        const auto& c_pad_view = [&]() {
-            const auto& c_tensor_view = views.at(I3);
-            if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
-            {
-                return pad_tensor_view(c_tensor_view,
-                                       make_tuple(number<TilePartitioner::MPerBlock>{},
-                                                  number<TilePartitioner::NPerBlock>{}),
-                                       sequence<false, GemmPipeline::kPadN>{});
-            }
-            else
-            {
-                return pad_tensor_view(c_tensor_view,
-                                       make_tuple(number<TilePartitioner::MPerBlock>{},
-                                                  number<TilePartitioner::NPerBlock>{}),
-                                       sequence<GemmPipeline::kPadM, false>{});
-            }
-        }();
-
-        return make_tuple(a_pad_view, bq_pad_view, b_pad_view, c_pad_view);
-    }
-
-    template <typename PadView>
-    CK_TILE_DEVICE static auto
-    MakeGemmTileWindows(const PadView& views, const index_t i_m, const index_t i_n)
-    {
-        const auto& a_pad_view  = views.at(I0);
-        const auto& bq_pad_view = views.at(I1);
-        const auto& b_pad_view  = views.at(I2);
-        const auto& c_pad_view  = views.at(I3);
-
-        const auto& a_block_window = [&]() {
-            if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
-            {
-                return make_tile_window(a_pad_view,
-                                        make_tuple(number<TilePartitioner::MPerBlock>{},
-                                                   number<TilePartitioner::KPerBlock>{}),
-                                        {i_m, 0});
-            }
-            else
-            {
-                return make_tile_window(a_pad_view,
-                                        make_tuple(number<TilePartitioner::KPerBlock>{},
-                                                   number<TilePartitioner::MPerBlock>{}),
-                                        {0, i_m});
-            }
-        }();
-
-        const auto& bq_block_window = [&]() {
-            static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
-            return make_tile_window(
-                bq_pad_view,
-                make_tuple(number<TilePartitioner::NPerBlock>{},
-                           number<TilePartitioner::KPerBlock / GemmPipeline::QuantGroupSize>{}),
-                {i_n, 0});
-        }();
-
-        const auto& b_block_window = [&]() {
-            if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::ColumnMajor>)
-            {
-                return make_tile_window(b_pad_view,
-                                        make_tuple(number<TilePartitioner::NPerBlock>{},
-                                                   number<TilePartitioner::KPerBlock>{}),
-                                        {i_n, 0});
-            }
-            else
-            {
-                return make_tile_window(b_pad_view,
-                                        make_tuple(number<TilePartitioner::KPerBlock>{},
-                                                   number<TilePartitioner::NPerBlock>{}),
-                                        {0, i_n});
-            }
-        }();
-
-        auto c_block_window = make_tile_window(
-            c_pad_view,
-            make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
-            {i_m, i_n});
-
-        return make_tuple(a_block_window, bq_block_window, b_block_window, c_block_window);
-    }
-
-    /**
-     * @brief Runs single GEMM problem cooperatively by whole workgroup.
-     *
-     * @param a_ptr input A pointer
-     * @param b_ptr input B pointer
-     * @param bq_ptr input BQ pointer
-     * @param c_ptr output C pointer
-     * @param smem_ptr_0 The start memory pointer of the shared memory block.
-     * @param kargs GEMM kernel arguments
-     * @param splitk_batch_offset splitk_batch_offset Utility structure used to calculate k batch.
-     * @param block_idx_m The GEMM's output M dimension tile index processed by this workgroup.
-     * @param block_idx_n The GEMM's output N dimension tile index processed by this workgroup.
-     *
-     * @tparam DstInMemOp Destination memory operation (default: set).
-     */
-    template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
-    CK_TILE_DEVICE static void RunGemm(const ADataType* a_ptr,
-                                       const BDataType* b_ptr,
-                                       const BQDataType* bq_ptr,
-                                       CDataType* c_ptr,
-                                       void* smem_ptr_0,
-                                       const BQuantGemmKernelArgs& kargs,
-                                       const SplitKBatchOffset& splitk_batch_offset,
-                                       const index_t block_idx_m,
-                                       const index_t block_idx_n)
-    {
-        // Create Gemm tensor views, pad views and tile windows
-        const auto& gemm_tensor_views_tuple = MakeGemmTensorViews<DstInMemOp>(
-            a_ptr, b_ptr, bq_ptr, c_ptr, kargs, splitk_batch_offset);
-
-        const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
-        auto gemm_tile_windows     = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
-
-        const index_t num_loop = __builtin_amdgcn_readfirstlane(
-            TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k));
-
-        // Run GEMM cooperatively by whole workgroup.
-        const auto& a_block_window  = gemm_tile_windows.at(I0);
-        const auto& bq_block_window = gemm_tile_windows.at(I1);
-        const auto& b_block_window  = gemm_tile_windows.at(I2);
-
-        const auto& c_block_tile = GemmPipeline{}.template operator()(
-            a_block_window, b_block_window, bq_block_window, num_loop, smem_ptr_0);
-
-        // Run Epilogue Pipeline
-        auto& c_block_window = gemm_tile_windows.at(I3);
-
-        EpiloguePipeline{}.template
-        operator()<decltype(c_block_window), decltype(c_block_tile), decltype(c_block_window)>(
-            c_block_window, c_block_tile, c_block_window, smem_ptr_0);
-    }
-
-    CK_TILE_DEVICE void operator()(BQuantGemmKernelArgs kargs) const
-    {
-        const auto blockId  = __builtin_amdgcn_readfirstlane(blockIdx.x);
-        const auto [iM, iN] = TilePartitioner{kargs.M, kargs.N}.GetOutputTileIndex(blockId);
-        const index_t i_m   = __builtin_amdgcn_readfirstlane(iM * TilePartitioner::MPerBlock);
-        const index_t i_n   = __builtin_amdgcn_readfirstlane(iN * TilePartitioner::NPerBlock);
-
-        const SplitKBatchOffset splitk_batch_offset(kargs);
-        // options
-        const ADataType* a_ptr   = static_cast<const ADataType*>(kargs.a_ptr);
-        const BDataType* b_ptr   = static_cast<const BDataType*>(kargs.b_ptr);
-        const BQDataType* bq_ptr = static_cast<const BQDataType*>(kargs.bq_ptr);
-        CDataType* c_ptr         = static_cast<CDataType*>(kargs.c_ptr);
-
-        // allocate LDS
-        __shared__ char smem_ptr_0[GetSmemSize()];
-
-        assert(kargs.k_batch == 1);
-        RunGemm(a_ptr, b_ptr, bq_ptr, c_ptr, smem_ptr_0, kargs, splitk_batch_offset, i_m, i_n);
-    }
-};
-
-} // namespace ck_tile
diff --git a/include/ck_tile/ops/gemm_group_quant/kernel/gemm_aquant_kernel.hpp b/include/ck_tile/ops/gemm_group_quant/kernel/gemm_quant_kernel.hpp
similarity index 57%
rename from include/ck_tile/ops/gemm_group_quant/kernel/gemm_aquant_kernel.hpp
rename to include/ck_tile/ops/gemm_group_quant/kernel/gemm_quant_kernel.hpp
index 69acb668ed..f844852d62 100644
--- a/include/ck_tile/ops/gemm_group_quant/kernel/gemm_aquant_kernel.hpp
+++ b/include/ck_tile/ops/gemm_group_quant/kernel/gemm_quant_kernel.hpp
@@ -12,117 +12,218 @@
 #include "ck_tile/core/numeric/integer.hpp"
 #include "ck_tile/core/numeric/math.hpp"
 #include "ck_tile/host/concat.hpp"
+#include "ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_quant_traits.hpp"
 
 namespace ck_tile {
 
-struct AQuantGemmProblem
+namespace detail {
+// Helper templates for safe type extraction
+template <typename T, typename Default>
+struct get_aq_layout_or
 {
-    CK_TILE_HOST AQuantGemmProblem() = default;
-    CK_TILE_HOST AQuantGemmProblem(index_t M_,
-                                   index_t N_,
-                                   index_t K_,
-                                   index_t QK_,
-                                   index_t stride_A_,
-                                   index_t stride_B_,
-                                   index_t stride_C_,
-                                   index_t stride_AQ_)
+    using type = Default;
+};
+
+template <typename T, typename Default>
+    requires requires { typename T::AQLayout; }
+struct get_aq_layout_or<T, Default>
+{
+    using type = typename T::AQLayout;
+};
+
+template <typename T, typename Default>
+struct get_bq_layout_or
+{
+    using type = Default;
+};
+
+template <typename T, typename Default>
+    requires requires { typename T::BQLayout; }
+struct get_bq_layout_or<T, Default>
+{
+    using type = typename T::BQLayout;
+};
+
+template <typename T, typename Default>
+struct get_aq_data_type_or
+{
+    using type = Default;
+};
+
+template <typename T, typename Default>
+    requires requires { typename T::AQDataType; }
+struct get_aq_data_type_or<T, Default>
+{
+    using type = typename T::AQDataType;
+};
+
+template <typename T, typename Default>
+struct get_bq_data_type_or
+{
+    using type = Default;
+};
+
+template <typename T, typename Default>
+    requires requires { typename T::BQDataType; }
+struct get_bq_data_type_or<T, Default>
+{
+    using type = typename T::BQDataType;
+};
+
+template <typename T, typename Default>
+struct get_preshuffle_or
+{
+    using type = Default;
+};
+
+template <typename T, typename Default>
+    requires requires { typename T::PreshuffleQuant; }
+struct get_preshuffle_or<T, Default>
+{
+    using type = typename T::PreshuffleQuant;
+};
+} // namespace detail
+
+struct QuantGemmProblem
+{
+    CK_TILE_HOST QuantGemmProblem() = default;
+    CK_TILE_HOST QuantGemmProblem(index_t M_,
+                                  index_t N_,
+                                  index_t K_,
+                                  index_t QK_A_,
+                                  index_t QK_B_,
+                                  index_t stride_A_,
+                                  index_t stride_B_,
+                                  index_t stride_C_,
+                                  index_t stride_AQ_,
+                                  index_t stride_BQ_)
         : M(M_),
           N(N_),
           K(K_),
-          QK(QK_),
+          QK_A(QK_A_),
+          QK_B(QK_B_),
           stride_A(stride_A_),
           stride_B(stride_B_),
           stride_C(stride_C_),
-          stride_AQ(stride_AQ_)
+          stride_AQ(stride_AQ_),
+          stride_BQ(stride_BQ_)
     {
     }
 
     index_t M;
     index_t N;
     index_t K;
-    index_t QK;
+    index_t QK_A;
+    index_t QK_B;
     index_t stride_A;
     index_t stride_B;
     index_t stride_C;
     index_t stride_AQ;
+    index_t stride_BQ;
 };
 
-struct AQuantGemmHostArgs : public AQuantGemmProblem
+struct QuantGemmHostArgs : public QuantGemmProblem
 {
-    CK_TILE_HOST AQuantGemmHostArgs() = default;
-    CK_TILE_HOST AQuantGemmHostArgs(const void* a_ptr_,
-                                    const void* b_ptr_,
-                                    void* c_ptr_,
-                                    const void* aq_ptr_,
-                                    index_t k_batch_,
-                                    index_t M_,
-                                    index_t N_,
-                                    index_t K_,
-                                    index_t QK_,
-                                    index_t stride_A_,
-                                    index_t stride_B_,
-                                    index_t stride_C_,
-                                    index_t stride_AQ_)
-        : AQuantGemmProblem(M_, N_, K_, QK_, stride_A_, stride_B_, stride_C_, stride_AQ_),
+    CK_TILE_HOST QuantGemmHostArgs() = default;
+    CK_TILE_HOST QuantGemmHostArgs(const void* a_ptr_,
+                                   const void* b_ptr_,
+                                   void* c_ptr_,
+                                   const void* aq_ptr_,
+                                   const void* bq_ptr_,
+                                   index_t k_batch_,
+                                   index_t M_,
+                                   index_t N_,
+                                   index_t K_,
+                                   index_t QK_A_,
+                                   index_t QK_B_,
+                                   index_t stride_A_,
+                                   index_t stride_B_,
+                                   index_t stride_C_,
+                                   index_t stride_AQ_,
+                                   index_t stride_BQ_)
+        : QuantGemmProblem(
+              M_, N_, K_, QK_A_, QK_B_, stride_A_, stride_B_, stride_C_, stride_AQ_, stride_BQ_),
           a_ptr(a_ptr_),
           b_ptr(b_ptr_),
           aq_ptr(aq_ptr_),
+          bq_ptr(bq_ptr_),
           c_ptr(c_ptr_),
           k_batch(k_batch_)
     {
     }
 
-    const void* a_ptr;
-    const void* b_ptr;
-    const void* aq_ptr;
-    void* c_ptr;
-    index_t k_batch;
+    const void* a_ptr  = nullptr;
+    const void* b_ptr  = nullptr;
+    const void* aq_ptr = nullptr;
+    const void* bq_ptr = nullptr;
+    void* c_ptr        = nullptr;
+    index_t k_batch    = 0;
 };
 
-struct AQuantGemmKernelArgs
+struct QuantGemmKernelArgs
 {
     const void* a_ptr;
     const void* b_ptr;
     const void* aq_ptr;
+    const void* bq_ptr;
     void* c_ptr;
     index_t M;
     index_t N;
     index_t K;
-    index_t QK;
+    index_t QK_A;
+    index_t QK_B;
     index_t stride_A;
     index_t stride_B;
     index_t stride_C;
     index_t stride_AQ;
+    index_t stride_BQ;
     index_t k_batch;
 };
 
-template <typename TilePartitioner_, typename GemmPipeline_, typename EpiloguePipeline_>
-struct AQuantGemmKernel
+template <typename TilePartitioner_,
+          typename GemmPipeline_,
+          typename EpiloguePipeline_,
+          QuantType QuantType_>
+struct QuantGemmKernel
 {
-    using TilePartitioner                 = remove_cvref_t<TilePartitioner_>;
-    using GemmPipeline                    = remove_cvref_t<GemmPipeline_>;
-    using EpiloguePipeline                = remove_cvref_t<EpiloguePipeline_>;
-    using ALayout                         = remove_cvref_t<typename GemmPipeline::ALayout>;
-    using AQLayout                        = remove_cvref_t<typename GemmPipeline::AQLayout>;
-    using BLayout                         = remove_cvref_t<typename GemmPipeline::BLayout>;
-    using CLayout                         = remove_cvref_t<typename GemmPipeline::CLayout>;
+    using TilePartitioner  = remove_cvref_t<TilePartitioner_>;
+    using GemmPipeline     = remove_cvref_t<GemmPipeline_>;
+    using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
+    using ALayout          = remove_cvref_t<typename GemmPipeline::ALayout>;
+    using BLayout          = remove_cvref_t<typename GemmPipeline::BLayout>;
+    using CLayout          = remove_cvref_t<typename GemmPipeline::CLayout>;
+
+    using AQLayout = remove_cvref_t<
+        typename detail::get_aq_layout_or<GemmPipeline, typename GemmPipeline::ALayout>::type>;
+    using BQLayout = remove_cvref_t<
+        typename detail::get_bq_layout_or<GemmPipeline, typename GemmPipeline::BLayout>::type>;
+
     static constexpr index_t kBlockSize   = GemmPipeline::BlockSize;
-    static constexpr bool PreshuffleQuant = GemmPipeline::PreshuffleQuant;
+    static constexpr bool PreshuffleQuant = remove_cvref_t<
+        typename detail::get_preshuffle_or<GemmPipeline, std::false_type>::type>::value;
 
-    using ADataType  = remove_cvref_t<typename GemmPipeline::ADataType>;
-    using AQDataType = remove_cvref_t<typename GemmPipeline::AQDataType>;
-    using BDataType  = remove_cvref_t<typename GemmPipeline::BDataType>;
-    using CDataType  = remove_cvref_t<typename EpiloguePipeline::ODataType>;
+    using ADataType   = remove_cvref_t<typename GemmPipeline::ADataType>;
+    using BDataType   = remove_cvref_t<typename GemmPipeline::BDataType>;
+    using CDataType   = remove_cvref_t<typename EpiloguePipeline::ODataType>;
+    using AccDataType = remove_cvref_t<typename EpiloguePipeline::AccDataType>;
 
-    static constexpr auto I0 = number<0>();
-    static constexpr auto I1 = number<1>();
-    static constexpr auto I2 = number<2>();
-    static constexpr auto I3 = number<3>();
+    using AQDataType =
+        remove_cvref_t<typename detail::get_aq_data_type_or<GemmPipeline, AccDataType>::type>;
+    using BQDataType =
+        remove_cvref_t<typename detail::get_bq_data_type_or<GemmPipeline, AccDataType>::type>;
+
+    static constexpr auto I0 = number<0>(); // A Tensor
+    static constexpr auto I1 = number<1>(); // AQ Tensor
+    static constexpr auto I2 = number<2>(); // B Tensor
+    static constexpr auto I3 = number<3>(); // BQ Tensor
+    static constexpr auto I4 = number<4>(); // C Tensor
+
+    static constexpr auto kQuantType = QuantType_;
 
     [[nodiscard]] CK_TILE_HOST static const std::string GetName()
     {
         // clang-format off
-        return concat('_', "gemm", gemm_prec_str<ADataType, BDataType>, GemmPipeline::GetName());
+        return concat('_', "gemm_quant", gemm_prec_str<ADataType, BDataType>, GemmPipeline::GetName());
         // clang-format on
     }
 
@@ -133,22 +234,25 @@ struct AQuantGemmKernel
 
     CK_TILE_HOST static constexpr auto BlockSize() { return dim3(kBlockSize); }
 
-    CK_TILE_HOST static constexpr AQuantGemmKernelArgs
-    MakeKernelArgs(const AQuantGemmHostArgs& hostArgs)
+    CK_TILE_HOST static constexpr QuantGemmKernelArgs
+    MakeKernelArgs(const QuantGemmHostArgs& hostArgs)
     {
-        return AQuantGemmKernelArgs{hostArgs.a_ptr,
-                                    hostArgs.b_ptr,
-                                    hostArgs.aq_ptr,
-                                    hostArgs.c_ptr,
-                                    hostArgs.M,
-                                    hostArgs.N,
-                                    hostArgs.K,
-                                    hostArgs.QK,
-                                    hostArgs.stride_A,
-                                    hostArgs.stride_B,
-                                    hostArgs.stride_C,
-                                    hostArgs.stride_AQ,
-                                    hostArgs.k_batch};
+        return QuantGemmKernelArgs{hostArgs.a_ptr,
+                                   hostArgs.b_ptr,
+                                   hostArgs.aq_ptr,
+                                   hostArgs.bq_ptr,
+                                   hostArgs.c_ptr,
+                                   hostArgs.M,
+                                   hostArgs.N,
+                                   hostArgs.K,
+                                   hostArgs.QK_A,
+                                   hostArgs.QK_B,
+                                   hostArgs.stride_A,
+                                   hostArgs.stride_B,
+                                   hostArgs.stride_C,
+                                   hostArgs.stride_AQ,
+                                   hostArgs.stride_BQ,
+                                   hostArgs.k_batch};
     }
 
     CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
@@ -158,7 +262,7 @@ struct AQuantGemmKernel
 
     struct SplitKBatchOffset
     {
-        __device__ SplitKBatchOffset(const AQuantGemmKernelArgs& kargs,
+        __device__ SplitKBatchOffset(const QuantGemmKernelArgs& kargs,
                                      const std::size_t k_id = blockIdx.z)
         {
             constexpr auto K1   = TilePartitioner::BlockGemmShape::WarpTile::at(I2);
@@ -198,7 +302,7 @@ struct AQuantGemmKernel
         index_t splitted_k;
     };
 
-    CK_TILE_HOST static bool IsSupportedArgument(const AQuantGemmKernelArgs& kargs)
+    CK_TILE_HOST static bool IsSupportedArgument(const QuantGemmKernelArgs& kargs)
     {
         if(kargs.k_batch != 1)
         {
@@ -209,14 +313,31 @@ struct AQuantGemmKernel
             return false;
         }
 
-        static_assert(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>);
-        if(kargs.QK % GemmPipeline::GetVectorSizeAQ() != 0)
+        if constexpr(kQuantType == QuantType::AQuantGrouped)
         {
-            if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
+            static_assert(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>);
+            if(kargs.QK_A % GemmPipeline::GetVectorSizeAQ() != 0)
             {
-                CK_TILE_ERROR("K is not a multiple of vector load size for A tensor!");
+                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
+                {
+                    CK_TILE_ERROR("K_A is not a multiple of vector load size for A tensor!");
+                }
+                return false;
+            }
+        }
+
+        // NOTE: no kernel currently uses BQuant like this:
+        if constexpr(kQuantType == QuantType::BQuantGrouped)
+        {
+            static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
+            if(kargs.QK_B % GemmPipeline::GetVectorSizeBQ() != 0)
+            {
+                if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
+                {
+                    CK_TILE_ERROR("K_B is not a multiple of vector load size for B tensor!");
+                }
+                return false;
             }
-            return false;
         }
 
         if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
@@ -350,8 +471,9 @@ struct AQuantGemmKernel
     CK_TILE_DEVICE static auto MakeGemmTensorViews(const ADataType* a_ptr,
                                                    const BDataType* b_ptr,
                                                    const AQDataType* aq_ptr,
+                                                   const BQDataType* bq_ptr,
                                                    CDataType* c_ptr,
-                                                   const AQuantGemmKernelArgs& kargs,
+                                                   const QuantGemmKernelArgs& kargs,
                                                    const SplitKBatchOffset& splitk_batch_offset)
     {
         static_assert(!TilePartitioner::BlockGemmShape::PermuteA, "Not implemented!");
@@ -380,71 +502,85 @@ struct AQuantGemmKernel
             return ck_tile::integer_least_multiple(length, alignment) - length;
         };
 
-        const auto& make_preshuffled_aq_tensor_view = [&]() {
-            const auto aq_x = kargs.M * GemmPipeline::KPerBlockAQ;
-            const auto aq_y = kargs.QK / GemmPipeline::KPerBlockAQ;
-
-            const auto aq_desc =
-                make_naive_tensor_descriptor(make_tuple(aq_y, aq_x),
-                                             make_tuple(aq_x, 1),
-                                             number<GemmPipeline::GetVectorSizeAQ()>{},
-                                             number<1>{});
-
-            const auto block_tile_size = GemmPipeline::MPerBlock * GemmPipeline::KPerBlockAQ;
-            const auto aq_pad0_desc    = transform_tensor_descriptor(
-                aq_desc,
-                make_tuple(make_pass_through_transform(aq_y),
-                           make_right_pad_transform(aq_x, get_padding_size(aq_x, block_tile_size))),
-                make_tuple(sequence<0>{}, sequence<1>{}),
-                make_tuple(sequence<0>{}, sequence<1>{}));
-
-            const auto pad_aq_x = aq_pad0_desc.get_lengths()[I1];
-            const auto wave_tile_size =
-                TilePartitioner::BlockGemmShape::WarpTile::at(I0) * GemmPipeline::KPerBlockAQ;
-            const auto wave_tile_count_x = ck_tile::integer_divide_ceil(pad_aq_x, wave_tile_size);
-            const auto aq_unmerge_pad0_desc = transform_tensor_descriptor(
-                aq_pad0_desc,
-                make_tuple(make_pass_through_transform(aq_y),
-                           make_unmerge_transform(make_tuple(wave_tile_count_x, wave_tile_size))),
-                make_tuple(sequence<0>{}, sequence<1>{}),
-                make_tuple(sequence<0>{}, sequence<1, 2>{}));
-
-            const auto aq_pad1_desc = transform_tensor_descriptor(
-                aq_unmerge_pad0_desc,
-                make_tuple(make_pass_through_transform(aq_y),
-                           make_pass_through_transform(wave_tile_count_x),
-                           make_right_pad_transform(
-                               wave_tile_size, get_padding_size(wave_tile_size, get_warp_size()))),
-                make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
-                make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}));
-
-            const auto pad_wave_size =
-                ck_tile::integer_least_multiple(wave_tile_size, get_warp_size());
-            const auto aq_merge_pad1_desc = transform_tensor_descriptor(
-                aq_pad1_desc,
-                make_tuple(make_merge_transform(make_tuple(aq_y, wave_tile_count_x)),
-                           make_pass_through_transform(pad_wave_size)),
-                make_tuple(sequence<0, 1>{}, sequence<2>{}),
-                make_tuple(sequence<0>{}, sequence<1>{}));
-
-            return make_tensor_view<address_space_enum::global>(aq_ptr, aq_merge_pad1_desc);
-        };
-
         const auto& aq_tensor_view = [&]() {
-            static_assert(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>);
-            if constexpr(PreshuffleQuant)
+            if constexpr(kQuantType == QuantType::AQuantGrouped && PreshuffleQuant)
             {
-                return make_preshuffled_aq_tensor_view();
+                static_assert(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>);
+                const auto aq_x = kargs.M * GemmPipeline::KPerBlockAQ;
+                const auto aq_y = kargs.QK_A / GemmPipeline::KPerBlockAQ;
+
+                const auto aq_desc =
+                    make_naive_tensor_descriptor(make_tuple(aq_y, aq_x),
+                                                 make_tuple(aq_x, 1),
+                                                 number<GemmPipeline::GetVectorSizeAQ()>{},
+                                                 number<1>{});
+
+                const auto block_tile_size = GemmPipeline::MPerBlock * GemmPipeline::KPerBlockAQ;
+                const auto aq_pad0_desc    = transform_tensor_descriptor(
+                    aq_desc,
+                    make_tuple(
+                        make_pass_through_transform(aq_y),
+                        make_right_pad_transform(aq_x, get_padding_size(aq_x, block_tile_size))),
+                    make_tuple(sequence<0>{}, sequence<1>{}),
+                    make_tuple(sequence<0>{}, sequence<1>{}));
+
+                const auto pad_aq_x = aq_pad0_desc.get_lengths()[I1];
+                const auto wave_tile_size =
+                    TilePartitioner::BlockGemmShape::WarpTile::at(I0) * GemmPipeline::KPerBlockAQ;
+                const auto wave_tile_count_x =
+                    ck_tile::integer_divide_ceil(pad_aq_x, wave_tile_size);
+                const auto aq_unmerge_pad0_desc = transform_tensor_descriptor(
+                    aq_pad0_desc,
+                    make_tuple(
+                        make_pass_through_transform(aq_y),
+                        make_unmerge_transform(make_tuple(wave_tile_count_x, wave_tile_size))),
+                    make_tuple(sequence<0>{}, sequence<1>{}),
+                    make_tuple(sequence<0>{}, sequence<1, 2>{}));
+
+                const auto aq_pad1_desc = transform_tensor_descriptor(
+                    aq_unmerge_pad0_desc,
+                    make_tuple(
+                        make_pass_through_transform(aq_y),
+                        make_pass_through_transform(wave_tile_count_x),
+                        make_right_pad_transform(
+                            wave_tile_size, get_padding_size(wave_tile_size, get_warp_size()))),
+                    make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
+                    make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}));
+
+                const auto pad_wave_size =
+                    ck_tile::integer_least_multiple(wave_tile_size, get_warp_size());
+                const auto aq_merge_pad1_desc = transform_tensor_descriptor(
+                    aq_pad1_desc,
+                    make_tuple(make_merge_transform(make_tuple(aq_y, wave_tile_count_x)),
+                               make_pass_through_transform(pad_wave_size)),
+                    make_tuple(sequence<0, 1>{}, sequence<2>{}),
+                    make_tuple(sequence<0>{}, sequence<1>{}));
+
+                return make_tensor_view<address_space_enum::global>(aq_ptr, aq_merge_pad1_desc);
             }
-            else
+            else if constexpr(kQuantType == QuantType::AQuantGrouped && !PreshuffleQuant)
             {
+                static_assert(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>);
                 return make_naive_tensor_view<address_space_enum::global>(
                     aq_ptr,
-                    make_tuple(kargs.M, kargs.QK),
+                    make_tuple(kargs.M, kargs.QK_A),
                     make_tuple(kargs.stride_AQ, 1),
                     number<GemmPipeline::GetVectorSizeAQ()>{},
                     number<1>{});
             }
+            else if constexpr(kQuantType == QuantType::RowColQuant)
+            {
+                return make_naive_tensor_view<address_space_enum::global>(
+                    aq_ptr,
+                    make_tuple(kargs.M, kargs.N),
+                    make_tuple(1, 0), // broadcasting over n
+                    number<1>{},
+                    number<1>{});
+            }
+            else
+            {
+                return nullptr; // TODO: use some other "empty" type for this
+            }
         }();
 
         const auto& b_tensor_view = [&]() {
@@ -510,6 +646,32 @@ struct AQuantGemmKernel
             }
         }();
 
+        const auto& bq_tensor_view = [&]() {
+            if constexpr(kQuantType == QuantType::RowColQuant)
+            {
+                return make_naive_tensor_view<address_space_enum::global>(
+                    bq_ptr,
+                    make_tuple(kargs.M, kargs.N),
+                    make_tuple(0, 1), // broadcasting over m
+                    number<1>{},
+                    number<1>{});
+            }
+            else if constexpr(kQuantType == QuantType::BQuantGrouped)
+            {
+                static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
+                return make_naive_tensor_view<address_space_enum::global>(
+                    bq_ptr,
+                    make_tuple(kargs.N, kargs.QK_B),
+                    make_tuple(kargs.stride_BQ, 1),
+                    number<GemmPipeline::GetVectorSizeBQ()>{},
+                    number<1>{});
+            }
+            else
+            {
+                return nullptr; // TODO: use some other "empty" type for this
+            }
+        }();
+
         // TODO: enable vector write for C in ColMajor
         const auto& c_tensor_view = [&]() {
             if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
@@ -532,7 +694,8 @@ struct AQuantGemmKernel
             }
         }();
 
-        return make_tuple(a_tensor_view, aq_tensor_view, b_tensor_view, c_tensor_view);
+        return make_tuple(
+            a_tensor_view, aq_tensor_view, b_tensor_view, bq_tensor_view, c_tensor_view);
     }
 
     template <typename TensorView>
@@ -556,6 +719,7 @@ struct AQuantGemmKernel
             }
         }();
 
+        // no padding
         const auto& aq_pad_view = [&]() { return views.at(I1); }();
 
         const auto& b_pad_view = [&]() {
@@ -576,9 +740,12 @@ struct AQuantGemmKernel
             }
         }();
 
+        // no padding
+        const auto& bq_pad_view = [&]() { return views.at(I3); }();
+
         // TODO vector write in for C in ColMajor
         const auto& c_pad_view = [&]() {
-            const auto& c_tensor_view = views.at(I3);
+            const auto& c_tensor_view = views.at(I4);
             if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
             {
                 return pad_tensor_view(c_tensor_view,
@@ -595,7 +762,7 @@ struct AQuantGemmKernel
             }
         }();
 
-        return make_tuple(a_pad_view, aq_pad_view, b_pad_view, c_pad_view);
+        return make_tuple(a_pad_view, aq_pad_view, b_pad_view, bq_pad_view, c_pad_view);
     }
 
     template <typename PadView>
@@ -605,7 +772,8 @@ struct AQuantGemmKernel
         const auto& a_pad_view  = views.at(I0);
         const auto& aq_pad_view = views.at(I1);
         const auto& b_pad_view  = views.at(I2);
-        const auto& c_pad_view  = views.at(I3);
+        const auto& bq_pad_view = views.at(I3);
+        const auto& c_pad_view  = views.at(I4);
 
         const auto& a_block_window = [&]() {
             if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
@@ -625,14 +793,13 @@ struct AQuantGemmKernel
         }();
 
         const auto& aq_block_window = [&]() {
-            static_assert(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>);
-            constexpr auto block_m = TilePartitioner::MPerBlock;
-            constexpr auto block_k = TilePartitioner::KPerBlock;
-            constexpr auto warp_m  = TilePartitioner::BlockGemmShape::WarpTile::at(I0);
-            constexpr auto aqk_per_block =
-                TilePartitioner::KPerBlock / GemmPipeline::QuantGroupSize;
-            if constexpr(PreshuffleQuant)
+            if constexpr(kQuantType == QuantType::AQuantGrouped && PreshuffleQuant)
             {
+                static_assert(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>);
+                constexpr auto block_m = TilePartitioner::MPerBlock;
+                constexpr auto warp_m  = TilePartitioner::BlockGemmShape::WarpTile::at(I0);
+                constexpr auto aqk_per_block =
+                    TilePartitioner::KPerBlock / GemmPipeline::QuantGroupSize;
                 constexpr auto tile_window_width =
                     ck_tile::integer_least_multiple(warp_m * aqk_per_block, get_warp_size());
                 constexpr auto tile_window_height = block_m / warp_m;
@@ -642,13 +809,27 @@ struct AQuantGemmKernel
                     make_tuple(number<tile_window_height>{}, number<tile_window_width>{}),
                     {block_m_idx * tile_window_height, 0});
             }
-            else
+            else if constexpr(kQuantType == QuantType::AQuantGrouped && !PreshuffleQuant)
             {
+                static_assert(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>);
+                constexpr auto block_m = TilePartitioner::MPerBlock;
+                constexpr auto block_k = TilePartitioner::KPerBlock;
                 return make_tile_window(
                     aq_pad_view,
                     make_tuple(number<block_m>{}, number<block_k / GemmPipeline::QuantGroupSize>{}),
                     {i_m, 0});
             }
+            else if constexpr(kQuantType == QuantType::RowColQuant)
+            {
+                return make_tile_window(aq_pad_view,
+                                        make_tuple(number<TilePartitioner::MPerBlock>{},
+                                                   number<TilePartitioner::NPerBlock>{}),
+                                        {i_m, i_n});
+            }
+            else
+            {
+                return nullptr; // TODO: use some other "empty" type?
+            }
         }();
 
         const auto& b_block_window = [&]() {
@@ -668,12 +849,36 @@ struct AQuantGemmKernel
             }
         }();
 
+        const auto& bq_block_window = [&]() {
+            if constexpr(kQuantType == QuantType::RowColQuant)
+            {
+                return make_tile_window(bq_pad_view,
+                                        make_tuple(number<TilePartitioner::MPerBlock>{},
+                                                   number<TilePartitioner::NPerBlock>{}),
+                                        {i_m, i_n});
+            }
+            else if constexpr(kQuantType == QuantType::BQuantGrouped)
+            {
+                static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
+                return make_tile_window(
+                    bq_pad_view,
+                    make_tuple(number<TilePartitioner::NPerBlock>{},
+                               number<TilePartitioner::KPerBlock / GemmPipeline::QuantGroupSize>{}),
+                    {i_n, 0});
+            }
+            else
+            {
+                return nullptr; // TODO: use some other "empty" type here
+            }
+        }();
+
         auto c_block_window = make_tile_window(
             c_pad_view,
             make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
             {i_m, i_n});
 
-        return make_tuple(a_block_window, aq_block_window, b_block_window, c_block_window);
+        return make_tuple(
+            a_block_window, aq_block_window, b_block_window, bq_block_window, c_block_window);
     }
 
     /**
@@ -695,16 +900,17 @@ struct AQuantGemmKernel
     CK_TILE_DEVICE static void RunGemm(const ADataType* a_ptr,
                                        const BDataType* b_ptr,
                                        const AQDataType* aq_ptr,
+                                       const BQDataType* bq_ptr,
                                        CDataType* c_ptr,
                                        void* smem_ptr_0,
-                                       const AQuantGemmKernelArgs& kargs,
+                                       const QuantGemmKernelArgs& kargs,
                                        const SplitKBatchOffset& splitk_batch_offset,
                                        const index_t block_idx_m,
                                        const index_t block_idx_n)
     {
         // Create Gemm tensor views, pad views and tile windows
         const auto& gemm_tensor_views_tuple = MakeGemmTensorViews<DstInMemOp>(
-            a_ptr, b_ptr, aq_ptr, c_ptr, kargs, splitk_batch_offset);
+            a_ptr, b_ptr, aq_ptr, bq_ptr, c_ptr, kargs, splitk_batch_offset);
 
         const auto& gemm_pad_views = MakeGemmPadViews(gemm_tensor_views_tuple);
         auto gemm_tile_windows     = MakeGemmTileWindows(gemm_pad_views, block_idx_m, block_idx_n);
@@ -713,22 +919,51 @@ struct AQuantGemmKernel
             TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k));
 
         // Run GEMM cooperatively by whole workgroup.
-        const auto& a_block_window  = gemm_tile_windows.at(I0);
-        const auto& aq_block_window = gemm_tile_windows.at(I1);
-        const auto& b_block_window  = gemm_tile_windows.at(I2);
+        const auto& a_block_window = gemm_tile_windows.at(I0);
+        const auto& b_block_window = gemm_tile_windows.at(I2);
 
-        const auto& c_block_tile = GemmPipeline{}.template operator()(
-            a_block_window, b_block_window, aq_block_window, kargs.M, num_loop, smem_ptr_0);
+        const auto& c_block_tile = [&]() {
+            if constexpr(kQuantType == QuantType::AQuantGrouped)
+            {
+                const auto& aq_block_window = gemm_tile_windows.at(I1);
+                return GemmPipeline{}.template operator()(
+                    a_block_window, b_block_window, aq_block_window, kargs.M, num_loop, smem_ptr_0);
+            }
+            else if constexpr(kQuantType == QuantType::BQuantGrouped)
+            {
+                const auto& bq_block_window = gemm_tile_windows.at(I3);
+                return GemmPipeline{}.template operator()(
+                    a_block_window, b_block_window, bq_block_window, num_loop, smem_ptr_0);
+            }
+            else if constexpr(kQuantType == QuantType::RowColQuant)
+            {
+                return GemmPipeline{}.template operator()(
+                    a_block_window, b_block_window, num_loop, smem_ptr_0);
+            }
+        }();
 
         // Run Epilogue Pipeline
-        auto& c_block_window = gemm_tile_windows.at(I3);
+        auto& c_block_window = gemm_tile_windows.at(I4);
 
-        EpiloguePipeline{}.template
-        operator()<decltype(c_block_window), decltype(c_block_tile), decltype(c_block_window)>(
-            c_block_window, c_block_tile, c_block_window, smem_ptr_0);
+        if constexpr(kQuantType == QuantType::AQuantGrouped ||
+                     kQuantType == QuantType::BQuantGrouped)
+        {
+            EpiloguePipeline{}(c_block_window, c_block_tile, c_block_window, smem_ptr_0);
+        }
+        else if constexpr(kQuantType == QuantType::RowColQuant)
+        {
+            const auto& aq_block_window = gemm_tile_windows.at(I1);
+            const auto& bq_block_window = gemm_tile_windows.at(I3);
+            EpiloguePipeline{}(c_block_window,
+                               c_block_tile,
+                               c_block_window,
+                               smem_ptr_0,
+                               aq_block_window,
+                               bq_block_window);
+        }
     }
 
-    CK_TILE_DEVICE void operator()(AQuantGemmKernelArgs kargs) const
+    CK_TILE_DEVICE void operator()(QuantGemmKernelArgs kargs) const
     {
         const auto blockId  = __builtin_amdgcn_readfirstlane(blockIdx.x);
         const auto [iM, iN] = TilePartitioner{kargs.M, kargs.N}.GetOutputTileIndex(blockId);
@@ -740,13 +975,15 @@ struct AQuantGemmKernel
         const ADataType* a_ptr   = static_cast<const ADataType*>(kargs.a_ptr);
         const BDataType* b_ptr   = static_cast<const BDataType*>(kargs.b_ptr);
         const AQDataType* aq_ptr = static_cast<const AQDataType*>(kargs.aq_ptr);
+        const BQDataType* bq_ptr = static_cast<const BQDataType*>(kargs.bq_ptr);
         CDataType* c_ptr         = static_cast<CDataType*>(kargs.c_ptr);
 
         // allocate LDS
         __shared__ char smem_ptr_0[GetSmemSize()];
 
         assert(kargs.k_batch == 1);
-        RunGemm(a_ptr, b_ptr, aq_ptr, c_ptr, smem_ptr_0, kargs, splitk_batch_offset, i_m, i_n);
+        RunGemm(
+            a_ptr, b_ptr, aq_ptr, bq_ptr, c_ptr, smem_ptr_0, kargs, splitk_batch_offset, i_m, i_n);
     }
 };
 
diff --git a/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_quant_pipeline_problem.hpp b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_quant_pipeline_problem.hpp
index 69b8cd901e..a2cef2d994 100644
--- a/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_quant_pipeline_problem.hpp
+++ b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_quant_pipeline_problem.hpp
@@ -14,6 +14,7 @@ namespace ck_tile {
 template <typename ADataType_,
           typename AQDataType_,
           typename BDataType_,
+          typename BQDataType_,
           typename CDataType_,
           typename BlockGemmShape_,
           typename Traits_,
@@ -23,12 +24,12 @@ template <typename ADataType_,
           GemmPipelineScheduler Scheduler_ = GemmPipelineScheduler::Intrawave,
           bool HasHotLoop_                 = true,
           TailNumber TailNum_              = TailNumber::Full>
-struct GemmAQuantPipelineProblemBase : public GemmPipelineProblemBase<ADataType_,
-                                                                      BDataType_,
-                                                                      CDataType_,
-                                                                      BlockGemmShape_,
-                                                                      Traits_,
-                                                                      ComputeDataType_>
+struct GemmQuantPipelineProblemBase : public GemmPipelineProblemBase<ADataType_,
+                                                                     BDataType_,
+                                                                     CDataType_,
+                                                                     BlockGemmShape_,
+                                                                     Traits_,
+                                                                     ComputeDataType_>
 {
     using Base = GemmPipelineProblemBase<ADataType_,
                                          BDataType_,
@@ -44,6 +45,7 @@ struct GemmAQuantPipelineProblemBase : public GemmPipelineProblemBase<ADataType_
     using typename Base::CDataType;
     using typename Base::ComputeDataType;
     using AQDataType = remove_cvref_t<AQDataType_>;
+    using BQDataType = remove_cvref_t<BQDataType_>;
 
     using BlockGemmShape = typename Base::BlockGemmShape;
 
@@ -63,6 +65,7 @@ struct GemmAQuantPipelineProblemBase : public GemmPipelineProblemBase<ADataType_
     using Base::VectorLoadSize;
 
     using AQLayout = remove_cvref_t<typename Traits::AQLayout>;
+    using BQLayout = remove_cvref_t<typename Traits::BQLayout>;
 
     static constexpr uint32_t kQuantGroupSize = QuantGroupSize_;
     static constexpr auto Scheduler           = Scheduler_;
@@ -75,7 +78,7 @@ struct GemmAQuantPipelineProblemBase : public GemmPipelineProblemBase<ADataType_
     [[nodiscard]] CK_TILE_HOST static const std::string GetName()
     {
         // clang-format off
-        return concat('_', "gemm_aquant_problem",
+        return concat('_', "gemm_quant_problem",
                       concat('x', VectorLoadSize, kBlockSize),
                       concat('x', kPadM, kPadN, kPadK),
                       Scheduler,
@@ -94,6 +97,13 @@ struct GemmAQuantPipelineProblemBase : public GemmPipelineProblemBase<ADataType_
         static_assert(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>);
         return kPadK ? 1 : GetAlignmentAQ();
     }();
+
+    CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentBQ()
+    {
+        return VectorLoadSize / sizeof(BQDataType);
+    }
+
+    static constexpr index_t VectorSizeBQ = []() { return kPadK ? 1 : GetAlignmentBQ(); }();
 };
 
 template <typename ADataType_,
@@ -108,18 +118,19 @@ template <typename ADataType_,
           GemmPipelineScheduler Scheduler_ = GemmPipelineScheduler::Intrawave,
           bool HasHotLoop_                 = true,
           TailNumber TailNum_              = TailNumber::Full>
-using GemmAQuantPipelineProblem = GemmAQuantPipelineProblemBase<ADataType_,
-                                                                AQDataType_,
-                                                                BDataType_,
-                                                                CDataType_,
-                                                                BlockGemmShape_,
-                                                                Traits_,
-                                                                QuantGroupSize_,
-                                                                TransposeC_,
-                                                                ComputeDataType_,
-                                                                Scheduler_,
-                                                                HasHotLoop_,
-                                                                TailNum_>;
+using GemmAQuantPipelineProblem = GemmQuantPipelineProblemBase<ADataType_,
+                                                               AQDataType_,
+                                                               BDataType_,
+                                                               void, // no BQDataType for AQuant
+                                                               CDataType_,
+                                                               BlockGemmShape_,
+                                                               Traits_,
+                                                               QuantGroupSize_,
+                                                               TransposeC_,
+                                                               ComputeDataType_,
+                                                               Scheduler_,
+                                                               HasHotLoop_,
+                                                               TailNum_>;
 
 template <typename ADataType_,
           typename BDataType_,
@@ -132,96 +143,42 @@ template <typename ADataType_,
           GemmPipelineScheduler Scheduler_ = GemmPipelineScheduler::Intrawave,
           bool HasHotLoop_                 = true,
           TailNumber TailNum_              = TailNumber::Full>
-struct GemmBQuantPipelineProblemBase : public GemmPipelineProblemBase<ADataType_,
-                                                                      BDataType_,
-                                                                      CDataType_,
-                                                                      BlockGemmShape_,
-                                                                      Traits_,
-                                                                      ComputeDataType_>
-{
-    using Base = GemmPipelineProblemBase<ADataType_,
-                                         BDataType_,
-                                         CDataType_,
-                                         BlockGemmShape_,
-                                         Traits_,
-                                         ComputeDataType_>;
-
-    using Traits = typename Base::Traits;
-
-    using typename Base::ADataType;
-    using typename Base::BDataType;
-    using typename Base::CDataType;
-    using typename Base::ComputeDataType;
-    using BQDataType = remove_cvref_t<BQDataType_>;
-
-    using BlockGemmShape = typename Base::BlockGemmShape;
-
-    using typename Base::ALayout;
-    using typename Base::BLayout;
-    using typename Base::CLayout;
-
-    static constexpr bool TransposeC = Traits::TransposeC;
-
-    using Base::kBlockSize;
-
-    using Base::kPadK;
-    using Base::kPadM;
-    using Base::kPadN;
-
-    using Base::DoubleSmemBuffer;
-    using Base::VectorLoadSize;
-
-    using BQLayout = remove_cvref_t<typename Traits::BQLayout>;
-
-    static constexpr uint32_t kQuantGroupSize = QuantGroupSize_;
-    static constexpr auto Scheduler           = Scheduler_;
-    static constexpr auto HasHotLoop          = HasHotLoop_;
-    static constexpr auto TailNum             = TailNum_;
-
-    static_assert(BlockGemmShape::kK % kQuantGroupSize == 0);
-    static_assert(Scheduler == GemmPipelineScheduler::Intrawave);
-
-    [[nodiscard]] CK_TILE_HOST static const std::string GetName()
-    {
-        // clang-format off
-        return concat('_', "gemm_bquant_problem",
-                      concat('x', VectorLoadSize, kBlockSize),
-                      concat('x', kPadM, kPadN, kPadK),
-                      Scheduler,
-                      "QuantGroupSize",
-                      kQuantGroupSize);
-        // clang-format on
-    }
-
-    CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentBQ()
-    {
-        return VectorLoadSize / sizeof(BQDataType);
-    }
-
-    static constexpr index_t VectorSizeBQ = []() { return kPadK ? 1 : GetAlignmentBQ(); }();
-};
+using GemmBQuantPipelineProblem = GemmQuantPipelineProblemBase<ADataType_,
+                                                               void, // no AQDataType for BQuant
+                                                               BDataType_,
+                                                               BQDataType_,
+                                                               CDataType_,
+                                                               BlockGemmShape_,
+                                                               Traits_,
+                                                               QuantGroupSize_,
+                                                               false, // no TransposeC
+                                                               ComputeDataType_,
+                                                               Scheduler_,
+                                                               HasHotLoop_,
+                                                               TailNum_>;
 
 template <typename ADataType_,
           typename BDataType_,
-          typename BQDataType_,
           typename CDataType_,
+          typename AccDataType_,
           typename BlockGemmShape_,
           typename Traits_,
-          uint32_t QuantGroupSize_,
-          typename ComputeDataType_        = ADataType_,
+          bool TransposeC_                 = false,
+          typename ComputeDataType_        = BDataType_,
           GemmPipelineScheduler Scheduler_ = GemmPipelineScheduler::Intrawave,
           bool HasHotLoop_                 = true,
           TailNumber TailNum_              = TailNumber::Full>
-using GemmBQuantPipelineProblem = GemmBQuantPipelineProblemBase<ADataType_,
-                                                                BDataType_,
-                                                                BQDataType_,
-                                                                CDataType_,
-                                                                BlockGemmShape_,
-                                                                Traits_,
-                                                                QuantGroupSize_,
-                                                                ComputeDataType_,
-                                                                Scheduler_,
-                                                                HasHotLoop_,
-                                                                TailNum_>;
-
+using GemmRowColQuantPipelineProblem = GemmQuantPipelineProblemBase<ADataType_,
+                                                                    AccDataType_,
+                                                                    BDataType_,
+                                                                    AccDataType_,
+                                                                    CDataType_,
+                                                                    BlockGemmShape_,
+                                                                    Traits_,
+                                                                    1, // no group size applicable
+                                                                    TransposeC_,
+                                                                    ComputeDataType_,
+                                                                    Scheduler_,
+                                                                    HasHotLoop_,
+                                                                    TailNum_>;
 } // namespace ck_tile
diff --git a/include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_quant_traits.hpp b/include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_quant_traits.hpp
index 05ce35ae59..60023e62e8 100644
--- a/include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_quant_traits.hpp
+++ b/include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_quant_traits.hpp
@@ -4,9 +4,17 @@
 #pragma once
 
 #include "ck_tile/core.hpp"
+#include <cstdint>
 
 namespace ck_tile {
 
+enum struct QuantType : std::uint16_t
+{
+    AQuantGrouped = 0,
+    BQuantGrouped = 1,
+    RowColQuant   = 2
+};
+
 template <bool kPadM_,
           bool kPadN_,
           bool kPadK_,
@@ -14,19 +22,24 @@ template <bool kPadM_,
           typename ALayout_,
           typename BLayout_,
           typename CLayout_,
-          typename AQLayout_ = ALayout_>
-struct TileGemmAQuantTraits
+          QuantType QuantType_,
+          typename AQLayout_ = ALayout_,
+          typename BQLayout_ = BLayout_>
+struct TileGemmQuantTraits
 {
     static constexpr bool kPadM = kPadM_;
     static constexpr bool kPadN = kPadN_;
     static constexpr bool kPadK = kPadK_;
 
+    static constexpr QuantType kQuantType = QuantType_;
+
     static constexpr int _VectorSize = 16;
 
     using ALayout  = ALayout_;
     using BLayout  = BLayout_;
     using CLayout  = CLayout_;
     using AQLayout = AQLayout_;
+    using BQLayout = BQLayout_;
 
     static constexpr bool TransposeC            = false;
     static constexpr bool UseStructuredSparsity = false;
@@ -35,31 +48,4 @@ struct TileGemmAQuantTraits
     static constexpr bool PreshuffleQuant = PreshuffleQuant_;
 };
 
-template <bool kPadM_,
-          bool kPadN_,
-          bool kPadK_,
-          bool PreshuffleQuant_,
-          typename ALayout_,
-          typename BLayout_,
-          typename CLayout_,
-          typename BQLayout_ = BLayout_>
-struct TileGemmBQuantTraits
-{
-    static constexpr bool kPadM = kPadM_;
-    static constexpr bool kPadN = kPadN_;
-    static constexpr bool kPadK = kPadK_;
-
-    static constexpr int _VectorSize = 16;
-
-    using ALayout  = ALayout_;
-    using BLayout  = BLayout_;
-    using CLayout  = CLayout_;
-    using BQLayout = BQLayout_;
-
-    static constexpr bool TransposeC            = false;
-    static constexpr bool UseStructuredSparsity = false;
-    static constexpr index_t NumWaveGroups      = 1;
-    static constexpr bool PreshuffleQuant       = PreshuffleQuant_;
-};
-
 } // namespace ck_tile
diff --git a/test/ck_tile/CMakeLists.txt b/test/ck_tile/CMakeLists.txt
index 695cad19bc..1dde6ccdf7 100644
--- a/test/ck_tile/CMakeLists.txt
+++ b/test/ck_tile/CMakeLists.txt
@@ -23,4 +23,5 @@ add_subdirectory(add_rmsnorm2d_rdquant)
 add_subdirectory(gemm_block_scale)
 add_subdirectory(utility)
 add_subdirectory(reduce)
+add_subdirectory(epilogue)
 add_subdirectory(atomic_add_op)
diff --git a/test/ck_tile/epilogue/CMakeLists.txt b/test/ck_tile/epilogue/CMakeLists.txt
new file mode 100644
index 0000000000..4103b9d4db
--- /dev/null
+++ b/test/ck_tile/epilogue/CMakeLists.txt
@@ -0,0 +1 @@
+add_gtest_executable(test_ck_tile_cshuffle_epilogue test_cshuffle_epilogue.cpp)
diff --git a/test/ck_tile/epilogue/test_cshuffle_epilogue.cpp b/test/ck_tile/epilogue/test_cshuffle_epilogue.cpp
new file mode 100644
index 0000000000..7b2071eb54
--- /dev/null
+++ b/test/ck_tile/epilogue/test_cshuffle_epilogue.cpp
@@ -0,0 +1,84 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "test_cshuffle_epilogue_util.hpp"
+#include <gtest/gtest.h>
+#include <hip/hip_runtime.h>
+
+using namespace ck_tile;
+
+class CShuffleEpilogueTest : public ::testing::Test
+{
+    protected:
+    void SetUp() override {}
+};
+
+TEST_F(CShuffleEpilogueTest, BasicHalfTest)
+{
+    // Basic test configuration with half_t data types
+    using ADataType   = ck_tile::half_t;
+    using BDataType   = ck_tile::half_t;
+    using AccDataType = float;
+    using ODataType   = ck_tile::half_t;
+
+    constexpr index_t kMPerBlock = 256;
+    constexpr index_t kNPerBlock = 256;
+    constexpr index_t MWave      = 2;
+    constexpr index_t NWave      = 2;
+    constexpr index_t MPerXdl    = 32;
+    constexpr index_t NPerXdl    = 32;
+    constexpr index_t KPerXdl    = 8;
+
+    using TestProblem = SimpleCShuffleEpilogueProblem<ADataType,
+                                                      BDataType,
+                                                      AccDataType,
+                                                      ODataType,
+                                                      kMPerBlock,
+                                                      kNPerBlock,
+                                                      MWave,
+                                                      NWave,
+                                                      MPerXdl,
+                                                      NPerXdl,
+                                                      KPerXdl>;
+
+    bool result = run_cshuffle_epilogue_test<TestProblem, kMPerBlock, kNPerBlock>();
+    EXPECT_TRUE(result) << "Basic CShuffleEpilogue test failed";
+}
+
+TEST_F(CShuffleEpilogueTest, BasicHalfTestWithScale)
+{
+    // Basic test configuration with half_t data types
+    using ADataType   = ck_tile::half_t;
+    using BDataType   = ck_tile::half_t;
+    using AccDataType = float;
+    using ODataType   = ck_tile::half_t;
+
+    constexpr index_t kMPerBlock = 256;
+    constexpr index_t kNPerBlock = 256;
+    constexpr index_t MWave      = 2;
+    constexpr index_t NWave      = 2;
+    constexpr index_t MPerXdl    = 32;
+    constexpr index_t NPerXdl    = 32;
+    constexpr index_t KPerXdl    = 8;
+
+    using TestProblem = SimpleCShuffleEpilogueProblem<ADataType,
+                                                      BDataType,
+                                                      AccDataType,
+                                                      ODataType,
+                                                      kMPerBlock,
+                                                      kNPerBlock,
+                                                      MWave,
+                                                      NWave,
+                                                      MPerXdl,
+                                                      NPerXdl,
+                                                      KPerXdl>;
+
+    bool result = run_cshuffle_epilogue_test<TestProblem, kMPerBlock, kNPerBlock>(true);
+    EXPECT_TRUE(result) << "Scale CShuffleEpilogue test failed";
+}
+
+int main(int argc, char** argv)
+{
+    ::testing::InitGoogleTest(&argc, argv);
+    return RUN_ALL_TESTS();
+}
diff --git a/test/ck_tile/epilogue/test_cshuffle_epilogue_util.hpp b/test/ck_tile/epilogue/test_cshuffle_epilogue_util.hpp
new file mode 100644
index 0000000000..c23957d802
--- /dev/null
+++ b/test/ck_tile/epilogue/test_cshuffle_epilogue_util.hpp
@@ -0,0 +1,191 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/host/hip_check_error.hpp"
+#include "ck_tile/ops/epilogue/cshuffle_epilogue.hpp"
+#include "ck_tile/ops/elementwise.hpp"
+#include "ck_tile/ops/gemm/warp/warp_gemm_dispatcher.hpp"
+#include "ck_tile/ops/common/tensor_layout.hpp"
+
+#include <iostream>
+#include <memory>
+#include <numeric>
+#include <random>
+#include <vector>
+#include <hip/hip_runtime.h>
+
+namespace ck_tile {
+
+// Simple test kernel to invoke the CShuffleEpilogue
+template <typename Problem, index_t M, index_t N, bool UseScale>
+__global__ void test_cshuffle_epilogue_kernel(typename Problem::ODataType* __restrict__ output_data,
+                                              float* m_scale,
+                                              float* n_scale)
+{
+    using Epilogue = CShuffleEpilogue<Problem>;
+
+    static_assert(Problem::kMPerBlock <= M && Problem::kNPerBlock <= N,
+                  "Block size must fit in tensor dimensions");
+
+    // Allocate shared memory for epilogue
+    __shared__ char smem[Epilogue::GetSmemSize()];
+
+    // Create accumulator tile
+    constexpr auto lds_distribution_encode =
+        make_static_tile_distribution(Epilogue::MakeLdsDistributionEncode());
+    auto acc_tile =
+        make_static_distributed_tensor<typename Epilogue::AccDataType>(lds_distribution_encode);
+
+    // Fill acc_tile with a simple pattern
+    auto& acc_buffer = acc_tile.get_thread_buffer();
+    acc_buffer[0]    = 2.0F;
+
+    // Create output tensor view
+    auto output_tensor_view =
+        make_naive_tensor_view<address_space_enum::global>(output_data,
+                                                           make_tuple(M, N),
+                                                           make_tuple(N, 1),
+                                                           number<Epilogue::GetVectorSizeC()>{},
+                                                           number<1>{});
+
+    // Create output tile window
+    auto output_tile_window =
+        make_tile_window(output_tensor_view,
+                         make_tuple(number<Problem::kMPerBlock>{}, number<Problem::kNPerBlock>{}),
+                         {0, 0});
+
+    // Create empty D tensors tuple (we're ignoring ds_dram_windows for this test)
+    auto empty_ds = make_tuple();
+
+    // Call the epilogue
+    if constexpr(UseScale)
+    {
+        const auto m_scale_window = make_tile_window(
+            make_naive_tensor_view<address_space_enum::global>(
+                m_scale, make_tuple(M, N), make_tuple(1, 0), number<1>{}, number<1>{}),
+            make_tuple(number<Problem::kMPerBlock>{}, number<Problem::kNPerBlock>{}),
+            {0, 0});
+        const auto n_scale_window = make_tile_window(
+            make_naive_tensor_view<address_space_enum::global>(
+                n_scale, make_tuple(M, N), make_tuple(0, 1), number<1>{}, number<1>{}),
+            make_tuple(number<Problem::kMPerBlock>{}, number<Problem::kNPerBlock>{}),
+            {0, 0});
+        Epilogue{}(output_tile_window, acc_tile, empty_ds, smem, m_scale_window, n_scale_window);
+    }
+    else
+    {
+        Epilogue{}(output_tile_window, acc_tile, empty_ds, smem);
+    }
+}
+
+// Test configuration helper
+template <typename ADataType,
+          typename BDataType,
+          typename AccDataType,
+          typename ODataType,
+          index_t kM,
+          index_t kN,
+          index_t MWave,
+          index_t NWave,
+          index_t MPerXdl,
+          index_t NPerXdl,
+          index_t KPerXdl>
+using SimpleCShuffleEpilogueProblem =
+    CShuffleEpilogueProblem<ADataType,
+                            BDataType,
+                            ck_tile::tuple<>, // Empty Ds datatype tuple
+                            AccDataType,
+                            ODataType,
+                            ck_tile::tuple<>,                   // Empty Ds layout
+                            tensor_layout::gemm::RowMajor,      // ELayout
+                            ck_tile::element_wise::PassThrough, // CDElementwise
+                            kM,
+                            kN,
+                            MWave,
+                            NWave,
+                            MPerXdl,
+                            NPerXdl,
+                            KPerXdl,
+                            false, // isCTransposed,
+                            memory_operation_enum::set>;
+
+template <typename Problem, index_t M, index_t N>
+bool run_cshuffle_epilogue_test(bool use_scale = false)
+{
+    using ODataType = typename Problem::ODataType;
+
+    constexpr index_t kMPerBlock = Problem::kMPerBlock;
+    constexpr index_t kNPerBlock = Problem::kNPerBlock;
+    constexpr index_t kBlockSize = Problem::kBlockSize;
+
+    std::cout << "Running CShuffleEpilogue test with M=" << M << ", N=" << N
+              << ", MPerBlock=" << kMPerBlock << ", NPerBlock=" << kNPerBlock
+              << ", BlockSize=" << kBlockSize << std::endl;
+
+    // Allocate host memory
+    const size_t output_size = M * N;
+
+    std::vector<ODataType> host_output(output_size, static_cast<ODataType>(0));
+
+    // Allocate device memory
+    ODataType* device_output;
+
+    HIP_CHECK_ERROR(hipMalloc(&device_output, output_size * sizeof(ODataType)));
+
+    HIP_CHECK_ERROR(hipMemcpy(
+        device_output, host_output.data(), output_size * sizeof(ODataType), hipMemcpyHostToDevice));
+
+    // Launch kernel
+    dim3 gridSize(1, 1, 1);
+    dim3 blockSize(kBlockSize, 1, 1);
+
+    if(use_scale)
+    {
+        float* m_scale;
+        float* n_scale;
+        std::vector<float> h_m_scale(M, 1.0F);
+        std::vector<float> h_n_scale(N, 1.0F);
+        h_n_scale[1] = 2.0F; // multiply one col only with 2
+        HIP_CHECK_ERROR(hipMalloc(&m_scale, M * sizeof(float)));
+        HIP_CHECK_ERROR(hipMalloc(&n_scale, N * sizeof(float)));
+        HIP_CHECK_ERROR(
+            hipMemcpy(m_scale, h_m_scale.data(), M * sizeof(float), hipMemcpyHostToDevice));
+        HIP_CHECK_ERROR(
+            hipMemcpy(n_scale, h_n_scale.data(), N * sizeof(float), hipMemcpyHostToDevice));
+        test_cshuffle_epilogue_kernel<Problem, M, N, true>
+            <<<gridSize, blockSize>>>(device_output, m_scale, n_scale);
+    }
+    else
+    {
+        test_cshuffle_epilogue_kernel<Problem, M, N, false>
+            <<<gridSize, blockSize>>>(device_output, nullptr, nullptr);
+    }
+
+    // Check for kernel launch errors
+    HIP_CHECK_ERROR(hipGetLastError());
+    HIP_CHECK_ERROR(hipDeviceSynchronize());
+
+    // Copy results back
+    HIP_CHECK_ERROR(hipMemcpy(
+        host_output.data(), device_output, output_size * sizeof(ODataType), hipMemcpyDeviceToHost));
+
+    // Basic verification - just check that output has a 2, and 4 if using scaling
+    bool has_2 =
+        type_convert<float>(host_output[0]) > 1.9F && type_convert<float>(host_output[0]) < 2.1F;
+    bool scale_has_4 = true;
+    if(use_scale)
+    {
+        scale_has_4 = type_convert<float>(host_output[1]) > 3.9F &&
+                      type_convert<float>(host_output[1]) < 4.1F;
+    }
+
+    // Cleanup
+    HIP_CHECK_ERROR(hipFree(device_output));
+
+    return has_2 && scale_has_4;
+}
+
+} // namespace ck_tile
diff --git a/test/ck_tile/gemm_block_scale/test_gemm_aquant_utils.hpp b/test/ck_tile/gemm_block_scale/test_gemm_aquant_utils.hpp
index 61bb1a8bdd..83a9e57878 100644
--- a/test/ck_tile/gemm_block_scale/test_gemm_aquant_utils.hpp
+++ b/test/ck_tile/gemm_block_scale/test_gemm_aquant_utils.hpp
@@ -240,4 +240,4 @@ auto create_args(int argc, char* argv[])
 }
 
 // host API
-float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::stream_config& s);
+float gemm_calc_aquant(const ck_tile::QuantGemmHostArgs& args, const ck_tile::stream_config& s);
diff --git a/test/ck_tile/gemm_block_scale/test_run_gemm_aquant_example.inc b/test/ck_tile/gemm_block_scale/test_run_gemm_aquant_example.inc
index 9ed42ff8d2..a870028a37 100644
--- a/test/ck_tile/gemm_block_scale/test_run_gemm_aquant_example.inc
+++ b/test/ck_tile/gemm_block_scale/test_run_gemm_aquant_example.inc
@@ -26,7 +26,7 @@ template <typename GemmConfig,
           typename BLayout,
           typename CLayout,
           uint32_t QuantGroupSize>
-float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::stream_config& s)
+float gemm_calc_aquant(const ck_tile::QuantGemmHostArgs& args, const ck_tile::stream_config& s)
 {
     constexpr bool kPadM = false;
     constexpr bool kPadN = false;
@@ -55,13 +55,14 @@ float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::s
 
     using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenGemmShape>;
 
-    using CodegenGemmTraits = ck_tile::TileGemmAQuantTraits<kPadM,
-                                                            kPadN,
-                                                            kPadK,
-                                                            GemmConfig::PreshuffleQuant,
-                                                            ALayout,
-                                                            BLayout,
-                                                            CLayout>;
+    using CodegenGemmTraits = ck_tile::TileGemmQuantTraits<kPadM,
+                                                           kPadN,
+                                                           kPadK,
+                                                           false, // preshuffle
+                                                           ALayout,
+                                                           BLayout,
+                                                           CLayout,
+                                                           ck_tile::QuantType::AQuantGrouped>;
 
     using GemmPipelineProblem = ck_tile::GemmPipelineProblemBase<ADataType,
                                                                  BDataType,
@@ -114,8 +115,10 @@ float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::s
                                                     K_Warp_Tile,
                                                     transposed_warp_gemm,
                                                     ck_tile::memory_operation_enum::set>>;
-        using Kernel =
-            ck_tile::AQuantGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
+        using Kernel = ck_tile::QuantGemmKernel<TilePartitioner,
+                                                CodegenGemmPipeline,
+                                                GemmEpilogue,
+                                                ck_tile::QuantType::AQuantGrouped>;
 
         auto kargs = Kernel::MakeKernelArgs(args);
 
@@ -185,7 +188,7 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
                   int n_warmup,
                   int n_repeat)
 {
-    ck_tile::AQuantGemmHostArgs args;
+    ck_tile::QuantGemmHostArgs args;
     args.a_ptr     = a_m_k_dev_buf.GetDeviceBuffer();
     args.aq_ptr    = aq_m_aqk_dev_buf.GetDeviceBuffer();
     args.b_ptr     = b_k_n_dev_buf.GetDeviceBuffer();
@@ -194,7 +197,7 @@ float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
     args.M         = M;
     args.N         = N;
     args.K         = K;
-    args.QK        = AQK;
+    args.QK_A      = AQK;
     args.stride_A  = stride_A;
     args.stride_B  = stride_B;
     args.stride_C  = stride_C;