diff --git a/CHANGELOG.md b/CHANGELOG.md
index 76fb46cdd9..8ae97b3d61 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -27,6 +27,7 @@ Documentation for Composable Kernel available at [https://rocm.docs.amd.com/proj
 * Added int8 support for CK_TILE GEMM.
 * Added support for elementwise kernel.
 * Added benchmarking support for tile engine GEMM Multi D.
+* Added block scaling support in CK_TILE GEMM, allowing flexible use of quantization matrices from either A or B operands.
 
 ### Optimized
 
diff --git a/example/ck_tile/38_block_scale_gemm/CMakeLists.txt b/example/ck_tile/38_block_scale_gemm/CMakeLists.txt
index 914fdac0e4..12cf874c73 100644
--- a/example/ck_tile/38_block_scale_gemm/CMakeLists.txt
+++ b/example/ck_tile/38_block_scale_gemm/CMakeLists.txt
@@ -8,9 +8,8 @@ list(APPEND EXAMPLE_GEMM_COMPILE_OPTIONS -mllvm -enable-noalias-to-md-conversion
 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_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()
     message(DEBUG "Skipping ck_tile quant gemm tests for current target")
 endif()
diff --git a/example/ck_tile/38_block_scale_gemm/README.md b/example/ck_tile/38_block_scale_gemm/README.md
index fc905790f1..6d6aec28c8 100644
--- a/example/ck_tile/38_block_scale_gemm/README.md
+++ b/example/ck_tile/38_block_scale_gemm/README.md
@@ -10,6 +10,7 @@ mkdir build && cd build
 ../script/cmake-ck-dev.sh  ../ <arch>
 # The aquant pipeline method on the gemm calculation
 make tile_example_gemm_aquant_basic -j
+make tile_example_gemm_bquant_basic -j
 ```
 This will result in an executable `build/bin/tile_example_gemm_aquant_basic`
 
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
index 744c844040..d5a38fe754 100644
--- a/example/ck_tile/38_block_scale_gemm/gemm_aquant_basic.cpp
+++ b/example/ck_tile/38_block_scale_gemm/gemm_aquant_basic.cpp
@@ -27,8 +27,6 @@ float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::s
     constexpr bool kPadN = false;
     constexpr bool kPadK = false;
 
-    constexpr int kBlockPerCu = 1;
-
     static_assert(std::is_same_v<CLayout, ck_tile::tensor_layout::gemm::RowMajor>);
 
     constexpr ck_tile::index_t M_Tile = GemmConfig::M_Tile;
@@ -139,7 +137,7 @@ float gemm_calc_aquant(const ck_tile::AQuantGemmHostArgs& args, const ck_tile::s
         }
 
         float ave_time = ck_tile::launch_kernel(
-            s, ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
+            s, ck_tile::make_kernel<GemmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
 
         return ave_time;
     };
@@ -207,7 +205,7 @@ int run_gemm_example(int argc, char* argv[])
                                                         ck_tile::fp8_t,
                                                         ck_tile::half_t,
                                                         ck_tile::fp8_t>{});
-        return run_gemm_example_prec_type<GemmConfig<ck_tile::pk_int4_t>, TypeConfig, 128>(
+        return run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>, TypeConfig, 128>(
             a_layout, b_layout, argc, argv);
     }
     else if(data_type == "i4bf8")
@@ -216,7 +214,7 @@ int run_gemm_example(int argc, char* argv[])
                                                         ck_tile::bf8_t,
                                                         ck_tile::half_t,
                                                         ck_tile::bf8_t>{});
-        return run_gemm_example_prec_type<GemmConfig<ck_tile::pk_int4_t>, TypeConfig, 128>(
+        return run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t>, TypeConfig, 128>(
             a_layout, b_layout, argc, argv);
     }
     else
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
new file mode 100644
index 0000000000..991c4841e4
--- /dev/null
+++ b/example/ck_tile/38_block_scale_gemm/gemm_bquant_basic.cpp
@@ -0,0 +1,229 @@
+// 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 ADataType,
+          typename BDataType,
+          typename BQDataType,
+          typename AccDataType,
+          typename CDataType,
+          typename ComputeDataType,
+          typename ALayout,
+          typename BLayout,
+          typename CLayout,
+          uint32_t QuantGroupSize>
+float gemm_calc_bquant(const ck_tile::BQuantGemmHostArgs& 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::TileGemmBQuantTraits<kPadM,
+                                                            kPadN,
+                                                            kPadK,
+                                                            GemmConfig::PreshuffleQuant,
+                                                            ALayout,
+                                                            BLayout,
+                                                            CLayout>;
+
+   using GemmPipelineProblem = ck_tile::GemmPipelineProblemBase<ADataType,
+                                                                 BDataType,
+                                                                 AccDataType,
+                                                                 CodegenGemmShape,
+                                                                 CodegenGemmTraits,
+                                                                 ComputeDataType>;
+
+    using BaseGemmPipeline = ck_tile::BaseBQuantGemmPipelineAgBgCrCompV3<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;
+
+        using CodegenPipelineProblem =
+            ck_tile::GemmBQuantPipelineProblem<ADataType,
+                                               BDataType,
+                                               BQDataType,
+                                               AccDataType,
+                                               CodegenGemmShape,
+                                               CodegenGemmTraits,
+                                               QuantGroupSize,
+                                               ComputeDataType,
+                                               ck_tile::GemmPipelineScheduler::Intrawave,
+                                               has_hot_loop_v,
+                                               tail_number_v>;
+        using CodegenGemmPipeline = ck_tile::BQuantGemmPipelineAgBgCrCompV3<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::BQuantGemmKernel<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_bquant_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::BDataType, ck_tile::pk_int4_t> ||
+                 std::is_same_v<typename TypeConfig::BDataType, ck_tile::fp8_t> ||
+                 std::is_same_v<typename TypeConfig::BDataType, ck_tile::bf8_t>)
+    {
+        if(a_layout == "R" && b_layout == "C")
+        {
+            return run_gemm_example_with_layouts<GemmConfig, TypeConfig, QuantGroupSize>(
+                argc, argv, 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 B.");
+    }
+
+    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 == "fp8i4")
+    {
+        using TypeConfig = decltype(GemmQuantTypeConfig<ck_tile::fp8_t,
+                                                        ck_tile::pk_int4_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 == "bf8i4")
+    {
+        using TypeConfig = decltype(GemmQuantTypeConfig<ck_tile::bf8_t,
+                                                        ck_tile::pk_int4_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_utils.hpp b/example/ck_tile/38_block_scale_gemm/gemm_utils.hpp
index 83a53e3c13..2188d23a32 100644
--- a/example/ck_tile/38_block_scale_gemm/gemm_utils.hpp
+++ b/example/ck_tile/38_block_scale_gemm/gemm_utils.hpp
@@ -81,11 +81,10 @@ struct GemmConfigBase
     static constexpr bool TransposeC            = false;
     static constexpr bool UseStructuredSparsity = false;
 
-    static constexpr int kBlockPerCu                         = 1;
     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 ck_tile::index_t NumWaveGroups = 1;
+    
     static constexpr bool PreshuffleQuant           = false;
     static constexpr bool DoubleSmemBuffer          = false;
 };
@@ -105,6 +104,8 @@ struct GemmConfigDecode : public GemmConfigBase
     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           = 1;
+
     static constexpr auto Scheduler            = ck_tile::GemmPipelineScheduler::Default;
     static constexpr ck_tile::index_t Pipeline = CK_TILE_PIPELINE_DECODE;
 };
@@ -145,6 +146,9 @@ struct GemmConfigPreshuffleQuant : public GemmConfigBase
     static constexpr ck_tile::index_t K_Warp_Tile =
         get_k_from_preshuffled_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_PRESHUFFLEQUANT;
     static constexpr bool PreshuffleQuant      = true;
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 8b045a2cf4..8cf77cb011 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
@@ -1,3 +1,4 @@
+
 // SPDX-License-Identifier: MIT
 // Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
 
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
new file mode 100644
index 0000000000..e3e11bb0a9
--- /dev/null
+++ b/example/ck_tile/38_block_scale_gemm/run_gemm_bquant_example.inc
@@ -0,0 +1,286 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+#include <bit>
+#include <random>
+
+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_bq(const ck_tile::HostTensor<T>& t, int block_bq_k)
+{
+    if(t.get_lengths().size() != 2)
+    {
+        throw std::runtime_error("Host tensor is not rank 2 tensor.");
+    }
+    int n_   = t.get_lengths()[0];
+    int bqk_ = t.get_lengths()[1];
+    if(bqk_ % block_bq_k != 0)
+    {
+        throw std::runtime_error("shuffle_aq needs a bqk of multiple times of block_bq_k.");
+    }
+    ck_tile::HostTensor<T> t_view({n_, bqk_ / block_bq_k, block_bq_k});
+    std::copy(t.begin(), t.end(), t_view.begin());
+    return ck_tile::reference_permute(t_view, {1, 0, 2});
+}
+
+template <typename GemmConfig,
+          typename ADataType,
+          typename BDataType,
+          typename BQDataType,
+          typename DsDataType,
+          typename AccDataType,
+          typename CDataType,
+          typename ALayout,
+          typename BLayout,
+          typename BQLayout,
+          typename DsLayout,
+          typename CLayout,
+          uint32_t QuantGroupSize,
+          typename CDEElementWise = ck_tile::element_wise::PassThrough>
+float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
+                  ck_tile::DeviceMem& b_k_n_dev_buf,
+                  ck_tile::DeviceMem& bq_bqk_n_dev_buf,
+                  ck_tile::DeviceMem& c_m_n_dev_buf,
+                  ck_tile::index_t M,
+                  ck_tile::index_t N,
+                  ck_tile::index_t K,
+                  ck_tile::index_t BQK,
+                  ck_tile::index_t stride_A,
+                  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::BQuantGemmHostArgs 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();
+    args.c_ptr     = c_m_n_dev_buf.GetDeviceBuffer();
+    args.k_batch   = kbatch;
+    args.M         = M;
+    args.N         = N;
+    args.K         = K;
+    args.QK        = BQK;
+    args.stride_A  = stride_A;
+    args.stride_B  = stride_B;
+    args.stride_C  = stride_C;
+    args.stride_BQ = stride_BQ;
+
+    float ave_time = gemm_calc_bquant<GemmConfig,
+                                      ADataType,
+                                      BDataType,
+                                      BQDataType,
+                                      AccDataType,
+                                      CDataType,
+                                      ADataType, // computeDatatype
+                                      ALayout,
+                                      BLayout,
+                                      CLayout,
+                                      QuantGroupSize>(
+        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(ADataType) * M * K + sizeof(BDataType) * N * K +
+                           sizeof(BQDataType) * BQK * N + sizeof(CDataType) * M * N;
+    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 << " StrideB =" << stride_B << " StrideBQ =" << stride_BQ
+              << " StrideC =" << stride_C << " A_Layout =" << ALayout::name
+              << " B_Layout =" << BLayout::name << " C_Layout =" << CLayout::name
+              << " A_Type = " << DataTypeTraits<ADataType>::name
+              << " B_Type = " << DataTypeTraits<BDataType>::name
+              << " BQ_Type = " << DataTypeTraits<BQDataType>::name
+              << " Acc_Type = " << DataTypeTraits<AccDataType>::name
+              << " C_Type = " << DataTypeTraits<CDataType>::name << " : " << ave_time << " ms, "
+              << tflops << " TFlops, " << gb_per_sec << " GB/s, " << std::endl;
+
+    return ave_time;
+}
+
+template <typename GemmConfig,
+          typename TypeConfig,
+          uint32_t QuantGroupSize,
+          typename ALayout,
+          typename BLayout,
+          typename BQLayout,
+          typename CLayout>
+int run_gemm_example_with_layouts(int argc,
+                                  char* argv[],
+                                  const ALayout a_layout                  = ALayout{},
+                                  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 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(K % QuantGroupSize != 0)
+    {
+        throw std::runtime_error("K must be aligned with QuantGroupSize");
+    }
+
+    ck_tile::index_t BQK = K / QuantGroupSize;
+
+    ck_tile::index_t stride_A  = arg_parser.get_int("stride_a");
+    ck_tile::index_t stride_B  = arg_parser.get_int("stride_b");
+    ck_tile::index_t stride_BQ = arg_parser.get_int("stride_q");
+    ck_tile::index_t stride_C  = arg_parser.get_int("stride_c");
+
+    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_BQ = ck_tile::get_default_stride(BQK, N, stride_BQ, is_row_major(bq_layout));
+    stride_C  = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
+
+    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<BQDataType> bq_bqk_n(
+        ck_tile::host_tensor_descriptor(BQK, N, stride_BQ, is_row_major(bq_layout)));
+    ck_tile::HostTensor<CDataType> c_m_n_dev_result(
+        ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
+
+    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<BDataType, ck_tile::pk_int4_t>)
+        {
+            ck_tile::FillUniformDistribution<ck_tile::pk_int4_t>{-5.0f, 5.0f, fill_seed(gen)}(
+                b_k_n);
+        }
+        else
+        {
+            ck_tile::FillUniformDistribution<BDataType>{-2.0f, 3.0f, fill_seed(gen)}(b_k_n);
+        }
+        ck_tile::FillUniformDistribution<BQDataType>{-2.0f, 2.0f, fill_seed(gen)}(bq_bqk_n);
+        ck_tile::FillUniformDistribution<ADataType>{-5.0f, 5.0f, fill_seed(gen)}(a_m_k);
+    }
+    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>(0x38)}(a_m_k);
+        ck_tile::FillConstant<BDataType>{static_cast<BDataType>(0x22)}(b_k_n);
+        ck_tile::FillConstant<BQDataType>{static_cast<BQDataType>(0.5f)}(bq_bqk_n);
+    }
+    else
+    {
+        a_m_k.SetZero();
+        b_k_n.SetZero();
+        bq_bqk_n.SetZero();
+    }
+
+    ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.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 bq_bqk_n_dev_buf(bq_bqk_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());
+
+    a_m_k_dev_buf.ToDevice(a_m_k.data());
+    b_k_n_dev_buf.ToDevice(b_k_n.data());
+    bq_bqk_n_dev_buf.ToDevice(bq_bqk_n.data());
+    c_m_n_dev_buf.SetZero();
+    c_m_n_dev_result.SetZero();
+
+    invoke_gemm<GemmConfig,
+                ADataType,
+                BDataType,
+                BQDataType,
+                ck_tile::tuple<>,
+                AccDataType,
+                CDataType,
+                ALayout,
+                BLayout,
+                BQLayout,
+                ck_tile::tuple<>,
+                CLayout,
+                QuantGroupSize>(a_m_k_dev_buf,
+                                b_k_n_dev_buf,
+                                bq_bqk_n_dev_buf,
+                                c_m_n_dev_buf,
+                                M,
+                                N,
+                                K,
+                                BQK,
+                                stride_A,
+                                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();
+
+        ck_tile::reference_gemm_quant<ADataType,
+                                      BQDataType,
+                                      BDataType,
+                                      AccDataType,
+                                      CDataType,
+                                      QuantGroupSize,
+                                      false>(a_m_k, bq_bqk_n, b_k_n, 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/core/numeric/pk_fp4.hpp b/include/ck_tile/core/numeric/pk_fp4.hpp
index 7464bc7c48..f25b98f5a0 100644
--- a/include/ck_tile/core/numeric/pk_fp4.hpp
+++ b/include/ck_tile/core/numeric/pk_fp4.hpp
@@ -250,8 +250,7 @@ CK_TILE_HOST_DEVICE constexpr pk_fp4_t fp16x2_to_pk_fp4(const fp16x2_t& x, float
 #if CK_TILE_FP4_CVT_DEVICE
     return impl::_to_f4(x, scale);
 #else
-    return pk_fp4_t::pack(float_to_e2m1(type_convert<float>(x[0]), scale),
-                          float_to_e2m1(type_convert<float>(x[1]), scale));
+    return pk_fp4_t::pack(float_to_e2m1(x[0], scale), float_to_e2m1(x[1], scale));
 #endif
 }
 CK_TILE_HOST_DEVICE constexpr pk_fp4_t bf16x2_to_pk_fp4(const bf16x2_t& x, float scale)
@@ -259,8 +258,7 @@ CK_TILE_HOST_DEVICE constexpr pk_fp4_t bf16x2_to_pk_fp4(const bf16x2_t& x, float
 #if CK_TILE_FP4_CVT_DEVICE
     return impl::_to_f4(x, scale);
 #else
-    return pk_fp4_t::pack(float_to_e2m1(type_convert<float>(x[0]), scale),
-                          float_to_e2m1(type_convert<float>(x[1]), scale));
+    return pk_fp4_t::pack(float_to_e2m1(x[0], scale), float_to_e2m1(x[1], scale));
 #endif
 }
 CK_TILE_HOST_DEVICE constexpr pk_fp4_t fp32x2_to_pk_fp4(const fp32x2_t& x, float scale)
diff --git a/include/ck_tile/ops/gemm_group_quant.hpp b/include/ck_tile/ops/gemm_group_quant.hpp
index 9f7565fefb..752da6a616 100644
--- a/include/ck_tile/ops/gemm_group_quant.hpp
+++ b/include/ck_tile/ops/gemm_group_quant.hpp
@@ -4,13 +4,18 @@
 #pragma once
 
 #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/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"
-#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_problem.hpp"
+#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_base.hpp"
+#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_policy.hpp"
+#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_v3.hpp"
+#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_quant_pipeline_problem.hpp"
 #include "ck_tile/ops/gemm_group_quant/pipeline/gemm_group_quant_utils.hpp"
-#include "ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_aquant_traits.hpp"
+#include "ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_quant_traits.hpp"
 #include "ck_tile/ops/common/generic_2d_block_shape.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
 #include "ck_tile/ops/common/utils.hpp"
diff --git a/include/ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_aquant_bs_cr.hpp b/include/ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_aquant_bs_cr.hpp
index 17ef73107b..614245f05b 100644
--- a/include/ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_aquant_bs_cr.hpp
+++ b/include/ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_aquant_bs_cr.hpp
@@ -12,7 +12,7 @@
 namespace ck_tile {
 
 template <typename Problem, index_t UnaryOpSize_ = 8>
-struct BlockGemmQuantBase
+struct BlockGemmAQuantBase
 {
     using AQDataType      = remove_cvref_t<typename Problem::AQDataType>;
     using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
@@ -67,7 +67,7 @@ struct BlockGemmQuantBase
 // B is block window on shared memory
 // C is block distributed tensor
 template <typename Problem_, typename Policy_ = BlockGemmASmemBSmemCRegV1DefaultPolicy>
-struct AQuantBlockUniversalGemmAsBsCr : public BlockGemmQuantBase<Problem_>
+struct AQuantBlockUniversalGemmAsBsCr : public BlockGemmAQuantBase<Problem_>
 {
     private:
     template <typename PipelineProblem_, typename GemmPolicy_>
@@ -103,13 +103,13 @@ struct AQuantBlockUniversalGemmAsBsCr : public BlockGemmQuantBase<Problem_>
         using I1 = number<1>;
 
         static_assert(MWarp == BlockGemmShape::BlockWarps::at(I0{}),
-                      "Error! WarpGemm's MWarp is not consisten with BlockGemmShape!");
+                      "Error! WarpGemm's MWarp is not consistent with BlockGemmShape!");
         static_assert(NWarp == BlockGemmShape::BlockWarps::at(I1{}),
-                      "Error! WarpGemm's NWarp is not consisten with BlockGemmShape!");
+                      "Error! WarpGemm's NWarp is not consistent with BlockGemmShape!");
         static_assert(WarpGemm::kM == BlockGemmShape::WarpTile::at(I0{}),
-                      "Error! WarpGemm's M is not consisten with BlockGemmShape!");
+                      "Error! WarpGemm's M is not consistent with BlockGemmShape!");
         static_assert(WarpGemm::kN == BlockGemmShape::WarpTile::at(I1{}),
-                      "Error! WarpGemm's N is not consisten with BlockGemmShape!");
+                      "Error! WarpGemm's N is not consistent with BlockGemmShape!");
 
         static constexpr index_t MIterPerWarp = MPerBlock / (MWarp * WarpGemm::kM);
         static constexpr index_t NIterPerWarp = NPerBlock / (NWarp * WarpGemm::kN);
@@ -170,7 +170,7 @@ struct AQuantBlockUniversalGemmAsBsCr : public BlockGemmQuantBase<Problem_>
     using ComputeDataType = remove_cvref_t<typename Traits::ComputeDataType>;
     using CDataType       = remove_cvref_t<typename Traits::CDataType>;
 
-    using Base = BlockGemmQuantBase<Problem_>;
+    using Base = BlockGemmAQuantBase<Problem_>;
 
     using WarpGemm = remove_cvref_t<typename Traits::WarpGemm>;
 
@@ -323,7 +323,7 @@ struct AQuantBlockUniversalGemmAsBsCr : public BlockGemmQuantBase<Problem_>
                                        [[maybe_unused]] BSmemBlockWindow& b_block_window)
         {
             static_assert(std::is_same_v<CDataType, typename CBlockTensor::DataType>,
-                          "The CDataType as defined in traits should be the same as correspoinding "
+                          "The CDataType as defined in traits should be the same as corresponding "
                           "C block tensor data type!");
             constexpr auto warp_size = get_warp_size();
 
diff --git a/include/ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_bs_bquant_cr.hpp b/include/ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_bs_bquant_cr.hpp
new file mode 100644
index 0000000000..844c8f6eb0
--- /dev/null
+++ b/include/ck_tile/ops/gemm_group_quant/block/block_universal_gemm_as_bs_bquant_cr.hpp
@@ -0,0 +1,439 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/core/arch/arch.hpp"
+#include "ck_tile/ops/gemm/block/block_gemm_asmem_bsmem_creg_v1_default_policy.hpp"
+#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
+#include "ck_tile/ops/elementwise.hpp"
+
+namespace ck_tile {
+
+template <typename Problem, index_t UnaryOpSize_ = 8>
+struct BlockGemmBQuantBase
+{
+    using BQDataType      = remove_cvref_t<typename Problem::BQDataType>;
+    using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
+
+    static constexpr index_t UnaryOpSize = UnaryOpSize_;
+    template <typename T>
+    CK_TILE_DEVICE static float cvt_scale_to_fp32(T scale)
+    {
+        float scale_reg_f = 0.f;
+        if constexpr(std::is_same_v<BQDataType, ck_tile::fp8_t>)
+        {
+            scale_reg_f =
+                ck_tile::element_wise::amd_assembly_fp8_to_fp32(static_cast<uint32_t>(scale));
+        }
+        else if constexpr(std::is_same_v<BQDataType, ck_tile::bf8_t>)
+        {
+            scale_reg_f =
+                ck_tile::element_wise::amd_assembly_bf8_to_fp32(static_cast<uint32_t>(scale));
+        }
+        else if constexpr(std::is_same_v<BQDataType, float>)
+        {
+            scale_reg_f = ck_tile::bit_cast<float>(scale);
+        }
+        else
+        {
+            static_assert(false, "BQDataType must be float, fp8_t or bf8_t.");
+        }
+        return scale_reg_f;
+    }
+
+    // can be inherited from A
+    template <typename WarpWindow, typename WarpTile>
+    CK_TILE_DEVICE static void load_interleaved_pk_type(WarpTile& warp_tile,
+                                                        const WarpWindow& warp_window)
+    {
+        const element_wise::PassThroughPack8 elementwise_op{};
+
+        static_assert(WarpTile::get_thread_buffer_size() % UnaryOpSize == 0);
+        constexpr index_t thread_buffer_size = WarpTile::get_thread_buffer_size() / UnaryOpSize;
+        const auto in_dstr_tensors           = load_tile(warp_window);
+
+        using ComputeVectorType = ComputeDataType __attribute__((ext_vector_type(UnaryOpSize)));
+        static_for<0, thread_buffer_size, 1>{}([&](auto i) {
+            elementwise_op(warp_tile.get_thread_buffer().template get_as<ComputeVectorType>()(i),
+                           in_dstr_tensors.get_thread_buffer().template get_as<pk_int4x4_t>()[i]);
+        });
+    }
+};
+
+// A is block window on shared memory
+// BQ (scale tensor) is block distributed tensor.
+// Consecutive kQuantGroupSize elements of B are quantized with a separate scale.
+// B is block window on shared memory
+// C is block distributed tensor
+template <typename Problem_, typename Policy_ = BlockGemmASmemBSmemCRegV1DefaultPolicy>
+struct BQuantBlockUniversalGemmAsBsCr : public BlockGemmBQuantBase<Problem_>
+{
+    private:
+    template <typename PipelineProblem_, typename GemmPolicy_>
+    struct GemmTraits_
+    {
+        using Problem         = remove_cvref_t<PipelineProblem_>;
+        using Policy          = remove_cvref_t<GemmPolicy_>;
+        using ADataType       = remove_cvref_t<typename Problem::ADataType>;
+        using BDataType       = remove_cvref_t<typename Problem::BDataType>;
+        using BQDataType      = remove_cvref_t<typename Problem::BQDataType>;
+        using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
+        using CDataType       = remove_cvref_t<typename Problem::CDataType>;
+        using BlockGemmShape  = remove_cvref_t<typename Problem::BlockGemmShape>;
+
+        static constexpr index_t kQuantGroupSize = Problem::kQuantGroupSize;
+        static constexpr index_t kBlockSize      = Problem::kBlockSize;
+        static constexpr auto Scheduler          = Problem::Scheduler;
+
+        // Threadblock GEMM tile size
+        static constexpr index_t MPerBlock  = BlockGemmShape::kM;
+        static constexpr index_t NPerBlock  = BlockGemmShape::kN;
+        static constexpr index_t KPerBlock  = BlockGemmShape::kK;
+        static constexpr index_t BQPerBlock = KPerBlock / kQuantGroupSize;
+
+        static constexpr auto config = Policy::template GetWarpGemmMWarpNWarp<Problem>();
+        using WarpGemm               = remove_cvref_t<decltype(config.template at<0>())>;
+
+        // number of warps along M and N for threadblock's GEMM problem size
+        static constexpr index_t MWarp = config.template at<1>();
+        static constexpr index_t NWarp = config.template at<2>();
+
+        using I0 = number<0>;
+        using I1 = number<1>;
+
+        static_assert(MWarp == BlockGemmShape::BlockWarps::at(I0{}),
+                      "Error! WarpGemm's MWarp is not consistent with BlockGemmShape!");
+        static_assert(NWarp == BlockGemmShape::BlockWarps::at(I1{}),
+                      "Error! WarpGemm's NWarp is not consistent with BlockGemmShape!");
+        static_assert(WarpGemm::kM == BlockGemmShape::WarpTile::at(I0{}),
+                      "Error! WarpGemm's M is not consistent with BlockGemmShape!");
+        static_assert(WarpGemm::kN == BlockGemmShape::WarpTile::at(I1{}),
+                      "Error! WarpGemm's N is not consistent with BlockGemmShape!");
+
+        static constexpr index_t MIterPerWarp = MPerBlock / (MWarp * WarpGemm::kM);
+        static constexpr index_t NIterPerWarp = NPerBlock / (NWarp * WarpGemm::kN);
+        static constexpr index_t KIterPerWarp = KPerBlock / WarpGemm::kK;
+
+        static constexpr index_t QScalesPerBlockRow =
+            (KPerBlock + kQuantGroupSize - 1) / kQuantGroupSize;
+        static constexpr index_t QScalesPerWarpGemmRow =
+            (WarpGemm::kK + kQuantGroupSize - 1) / kQuantGroupSize;
+
+        static constexpr index_t KIterPerQScale = KIterPerWarp / QScalesPerBlockRow;
+
+        static_assert(kQuantGroupSize % WarpGemm::kK == 0,
+                      "Error! WarpGemm::kK should be a multiple of kQuantGroupSize");
+        static_assert(QScalesPerWarpGemmRow == 1,
+                      "Error! kQuantGroupSize shouldn't be smaller than WarpGemm::kK");
+        static_assert(KIterPerWarp % QScalesPerBlockRow == 0,
+                      "Error! KItersPerWarp should be a multiple of QscalesPerBlockRow");
+
+        static_assert(KPerBlock / kQuantGroupSize > 0,
+                      "Error! Each row of blockgemm should have a separate scale");
+
+        static_assert(MIterPerWarp * MWarp * WarpGemm::kM == MPerBlock,
+                      "Error! Warps should cover all Block tile!");
+        static_assert(NIterPerWarp * NWarp * WarpGemm::kN == NPerBlock,
+                      "Error! Warps should cover all Block tile!");
+
+        // Currently tested combinations (A, B, BQ)
+        // 1. fp8, fp8, fp32 -> f32
+        // 2. bf8, bf8, fp32 -> f32
+        // 3. i4,  fp8, (fp8/fp32) -> f32
+        // 4. i4,  bf8, (fp8/fp32) -> f32
+        static_assert((std::is_same_v<ADataType, fp8_t> || std::is_same_v<ADataType, bf8_t>) &&
+                      (std::is_same_v<BDataType, fp8_t> || std::is_same_v<BDataType, bf8_t> ||
+                       std::is_same_v<BDataType, ck_tile::pk_int4_t>) &&
+                      (std::is_same_v<BQDataType, float> ||
+                       std::is_same_v<BQDataType, ck_tile::fp8_t> ||
+                       std::is_same_v<BQDataType, ck_tile::bf8_t>) &&
+                      (std::is_same_v<ComputeDataType, fp8_t> ||
+                       std::is_same_v<ComputeDataType, bf8_t>) &&
+                      std::is_same_v<CDataType, fp32_t>);
+
+        static constexpr index_t InterWaveSchedulingMacClusters = 1;
+
+        static constexpr index_t KPack      = WarpGemm::kKPerThread;
+        static constexpr index_t KPerThread = KIterPerWarp * WarpGemm::kKPerThread;
+    };
+
+    public:
+    using Traits = GemmTraits_<Problem_, Policy_>;
+
+    using ADataType       = remove_cvref_t<typename Traits::ADataType>;
+    using BDataType       = remove_cvref_t<typename Traits::BDataType>;
+    using BQDataType      = remove_cvref_t<typename Traits::BQDataType>;
+    using ComputeDataType = remove_cvref_t<typename Traits::ComputeDataType>;
+    using CDataType       = remove_cvref_t<typename Traits::CDataType>;
+
+    using Base = BlockGemmBQuantBase<Problem_>;
+
+    using WarpGemm = remove_cvref_t<typename Traits::WarpGemm>;
+
+    static constexpr index_t KIterPerWarp = Traits::KIterPerWarp;
+    static constexpr index_t MIterPerWarp = Traits::MIterPerWarp;
+    static constexpr index_t NIterPerWarp = Traits::NIterPerWarp;
+
+    static constexpr index_t MWarp = Traits::MWarp;
+    static constexpr index_t NWarp = Traits::NWarp;
+
+    static constexpr auto Scheduler       = Traits::Scheduler;
+    static constexpr uint8_t kA_cvt_scale = std::is_same_v<ADataType, pk_int4_t> ? 16 : 1;
+    static constexpr uint8_t kB_cvt_scale = std::is_same_v<BDataType, pk_int4_t> ? 16 : 1;
+
+    using AWarpDstr = typename WarpGemm::AWarpDstr;
+    using BWarpDstr = typename WarpGemm::BWarpDstr;
+    using CWarpDstr = typename WarpGemm::CWarpDstr;
+
+    using AWarpTensor = typename WarpGemm::AWarpTensor;
+    using BWarpTensor = typename WarpGemm::BWarpTensor;
+    using CWarpTensor = typename WarpGemm::CWarpTensor;
+
+    static_assert(std::is_same_v<typename WarpGemm::CDataType, float>);
+
+    static constexpr auto a_warp_y_lengths =
+        to_sequence(AWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
+    static constexpr auto b_warp_y_lengths =
+        to_sequence(BWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
+    static constexpr auto c_warp_y_lengths =
+        to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
+
+    static constexpr auto a_warp_y_index_zeros = uniform_sequence_gen_t<AWarpDstr::NDimY, 0>{};
+    static constexpr auto b_warp_y_index_zeros = uniform_sequence_gen_t<BWarpDstr::NDimY, 0>{};
+    static constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
+
+    static constexpr index_t APackedSize =
+        ck_tile::numeric_traits<remove_cvref_t<ADataType>>::PackedSize;
+    static constexpr index_t BPackedSize =
+        ck_tile::numeric_traits<remove_cvref_t<BDataType>>::PackedSize;
+
+    using I0 = number<0>;
+    using I1 = number<1>;
+
+    CK_TILE_DEVICE static constexpr auto MakeABlockDistributionEncode()
+    {
+        constexpr index_t KPerThread     = Traits::KPerThread;
+        constexpr index_t NumMacClusters = Traits::InterWaveSchedulingMacClusters;
+
+        constexpr index_t KPerInnerLoop =
+            ck_tile::max(KPerThread / NumMacClusters, WarpGemm::kKPerThread);
+
+        constexpr index_t KIterInterwave = KPerInnerLoop / WarpGemm::kKPerThread;
+
+        using KIterSeq = std::conditional_t<Scheduler == GemmPipelineScheduler::Interwave,
+                                            sequence<KIterInterwave>,
+                                            sequence<KIterPerWarp>>;
+
+        constexpr auto a_block_outer_dstr_encoding =
+            tile_distribution_encoding<sequence<NWarp>,
+                                       tuple<sequence<MIterPerWarp, MWarp>, KIterSeq>,
+                                       tuple<sequence<1, 0>>,
+                                       tuple<sequence<1, 0>>,
+                                       sequence<1, 2>,
+                                       sequence<0, 0>>{};
+        constexpr auto a_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
+            a_block_outer_dstr_encoding, typename WarpGemm::AWarpDstrEncoding{});
+
+        return a_block_dstr_encode;
+    }
+
+    CK_TILE_DEVICE static constexpr auto MakeBBlockDistributionEncode()
+    {
+        constexpr index_t KPerThread     = Traits::KPerThread;
+        constexpr index_t NumMacClusters = Traits::InterWaveSchedulingMacClusters;
+        constexpr index_t KPerInnerLoop =
+            ck_tile::max(KPerThread / NumMacClusters, WarpGemm::kKPerThread);
+        constexpr index_t KIterInterwave = KPerInnerLoop / WarpGemm::kKPerThread;
+
+        using KIterSeq = std::conditional_t<Scheduler == GemmPipelineScheduler::Interwave,
+                                            sequence<KIterInterwave>,
+                                            sequence<KIterPerWarp>>;
+
+        constexpr auto b_block_outer_dstr_encoding =
+            tile_distribution_encoding<sequence<MWarp>,
+                                       tuple<sequence<NIterPerWarp, NWarp>, KIterSeq>,
+                                       tuple<sequence<0, 1>>,
+                                       tuple<sequence<0, 1>>,
+                                       sequence<1, 2>,
+                                       sequence<0, 0>>{};
+
+        constexpr auto b_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
+            b_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
+
+        return b_block_dstr_encode;
+    }
+
+    private:
+    template <GemmPipelineScheduler Scheduler, typename GemmTraits>
+    struct BlockGemmImpl
+    {
+    };
+
+    template <typename GemmTraits>
+    struct BlockGemmImpl<GemmPipelineScheduler::Intrawave, GemmTraits>
+    {
+        static constexpr auto ALdsTileDistr =
+            decltype(make_static_tile_distribution(MakeABlockDistributionEncode())){};
+        static constexpr auto BLdsTileDistr =
+            decltype(make_static_tile_distribution(MakeBBlockDistributionEncode())){};
+
+        using ALdsTile = decltype(make_static_distributed_tensor<ComputeDataType>(ALdsTileDistr));
+        using BLdsTile = decltype(make_static_distributed_tensor<ComputeDataType>(BLdsTileDistr));
+
+        ALdsTile a_warp_tile_;
+        BLdsTile b_warp_tile_;
+
+        template <typename ASmemBlockWindow, typename BSmemBlockWindow>
+        CK_TILE_DEVICE void LocalPrefetch(const ASmemBlockWindow& a_block_window,
+                                          const BSmemBlockWindow& b_block_window)
+        {
+            if constexpr(std::is_same_v<ADataType, pk_int4_t>)
+            {
+                static_assert(std::is_same_v<ComputeDataType, fp8_t> ||
+                              std::is_same_v<ComputeDataType, bf8_t>);
+                Base::load_interleaved_pk_type(a_warp_tile_, a_block_window);
+            }
+            else
+            {
+                load_tile(a_warp_tile_, a_block_window);
+            }
+            if constexpr(std::is_same_v<BDataType, pk_int4_t>)
+            {
+                static_assert(std::is_same_v<ComputeDataType, fp8_t> ||
+                              std::is_same_v<ComputeDataType, bf8_t>);
+                Base::load_interleaved_pk_type(b_warp_tile_, b_block_window);
+            }
+            else
+            {
+                load_tile(b_warp_tile_, b_block_window);
+            }
+        }
+
+        // C += A * B
+        template <typename CBlockTensor,
+                  typename BQBlockTensor,
+                  typename ASmemBlockWindow,
+                  typename BSmemBlockWindow>
+        CK_TILE_DEVICE void operator()(CBlockTensor& c_block_tensor,
+                                       BQBlockTensor& bq_block_tensor,
+                                       [[maybe_unused]] ASmemBlockWindow& a_block_window,
+                                       [[maybe_unused]] BSmemBlockWindow& b_block_window)
+        {
+            static_assert(std::is_same_v<CDataType, typename CBlockTensor::DataType>,
+                          "The CDataType as defined in traits should be the same as corresponding "
+                          "C block tensor data type!");
+
+            // hot loop:
+            static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
+                static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
+                    CWarpTensor c_warp_tensor;
+
+                    static_for<0, Traits::QScalesPerBlockRow, 1>{}([&](auto kQScale) {
+                        static_for<0, Traits::KIterPerQScale, 1>{}([&](auto kIterInQScale) {
+                            constexpr auto kIter = kQScale * Traits::KIterPerQScale + kIterInQScale;
+
+                            AWarpTensor a_warp_tensor;
+                            a_warp_tensor.get_thread_buffer() =
+                                a_warp_tile_.get_y_sliced_thread_data(
+                                    merge_sequences(sequence<mIter, kIter>{}, a_warp_y_index_zeros),
+                                    merge_sequences(sequence<1, 1>{}, a_warp_y_lengths));
+
+                            BWarpTensor b_warp_tensor;
+                            b_warp_tensor.get_thread_buffer() =
+                                b_warp_tile_.get_y_sliced_thread_data(
+                                    merge_sequences(sequence<nIter, kIter>{}, b_warp_y_index_zeros),
+                                    merge_sequences(sequence<1, 1>{}, b_warp_y_lengths));
+
+                            if constexpr(kIterInQScale == 0)
+                            {
+                                c_warp_tensor = WarpGemm{}(a_warp_tensor, b_warp_tensor);
+                            }
+                            else
+                            {
+                                WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
+                            }
+                        });
+
+                        // Need to multiply bquant with accumulated C
+                        //
+                        // The accumulated C tile has the standard distribution. For example
+                        // lane 0 holds elements [0,0], [1,0], [2,0], [3,0], [8,0], [9,0],
+                        // [10,0], [11,0], [16,0], [17,0], [18,0], [19,0], [24,0], [25,0],
+                        // [26,0], [27,0].
+                        //
+                        // These elements are in different rows, need to get the scale value
+                        // for the corresponding row.
+                        // Based on bquant's tile distribution, it can be inferred which
+                        // lane holds the relevant scale. For example, the scales corresponding
+                        // to the 16 elements held by lane 0 are held by lanes 0, 1, 2, 3, 8, 9,
+                        // 10, 11, 16, 17, 18, 19, 24, 25, 26, 27 respectively.
+                        //
+                        // These scales can be obtained using __builtin_amdgcn_ds_bpermute.
+
+                        constexpr index_t reg_offset = nIter * Traits::BQPerBlock + kQScale;
+
+                        constexpr auto tbuf_offset =
+                            number<typename CBlockTensor::ThreadTensorDesc{}.calculate_offset(
+                                       merge_sequences(sequence<mIter, nIter>{},
+                                                       c_warp_y_index_zeros)) /
+                                   CBlockTensor::PackedSize>{};
+
+                        auto& scale_reg   = bq_block_tensor.get_thread_buffer()[reg_offset];
+                        float scale_reg_f = Base::cvt_scale_to_fp32(scale_reg);
+                        static_for<0, WarpGemm::kM / 2, 1>{}([&](auto c_row) {
+                            c_block_tensor.get_thread_buffer()[tbuf_offset + c_row] +=
+                                (c_warp_tensor.get_thread_buffer()[c_row] * scale_reg_f *
+                                 kA_cvt_scale * kB_cvt_scale);
+                        });
+                    });
+                });
+            });
+        }
+    };
+
+    public:
+    CK_TILE_DEVICE static constexpr auto MakeCBlockTile()
+    {
+        constexpr auto c_block_outer_dstr_encoding = tile_distribution_encoding<
+            sequence<>,
+            tuple<sequence<MIterPerWarp, MWarp>, sequence<NIterPerWarp, NWarp>>,
+            tuple<sequence<1, 2>>,
+            tuple<sequence<1, 1>>,
+            sequence<1, 2>,
+            sequence<0, 0>>{};
+
+        constexpr auto c_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
+            c_block_outer_dstr_encoding, typename WarpGemm::CWarpDstrEncoding{});
+        constexpr auto c_block_dstr = make_static_tile_distribution(c_block_dstr_encode);
+        auto c_block_tensor         = make_static_distributed_tensor<CDataType>(c_block_dstr);
+
+        return c_block_tensor;
+    }
+
+    template <typename ASmemBlockWindow, typename BSmemBlockWindow>
+    CK_TILE_DEVICE void LocalPrefetch(const ASmemBlockWindow& a_block_window,
+                                      const BSmemBlockWindow& b_block_window)
+    {
+        block_gemm_impl_.LocalPrefetch(a_block_window, b_block_window);
+    }
+
+    // C += A * B
+    template <typename CBlockTensor,
+              typename BQBlockTensor,
+              typename ASmemBlockWindow,
+              typename BSmemBlockWindow>
+    CK_TILE_DEVICE void operator()(CBlockTensor& c_block_tensor,
+                                   BQBlockTensor& bq_block_tensor,
+                                   const ASmemBlockWindow& a_block_window,
+                                   const BSmemBlockWindow& b_block_window)
+    {
+        block_gemm_impl_(c_block_tensor, bq_block_tensor, a_block_window, b_block_window);
+    }
+
+    private:
+    BlockGemmImpl<Scheduler, Traits> block_gemm_impl_{};
+};
+
+} // namespace ck_tile
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
new file mode 100644
index 0000000000..08b0ec0c2c
--- /dev/null
+++ b/include/ck_tile/ops/gemm_group_quant/kernel/gemm_bquant_kernel.hpp
@@ -0,0 +1,679 @@
+// 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/pipeline/gemm_aquant_pipeline_ag_bg_cr_policy.hpp b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_ag_bg_cr_policy.hpp
index 5f15a15a45..52c99f8e99 100644
--- a/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_ag_bg_cr_policy.hpp
+++ b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_ag_bg_cr_policy.hpp
@@ -28,7 +28,7 @@ struct GemmAQuantPipelineAgBgCrDefaultPolicy : public UniversalGemmPipelineAgBgC
         constexpr index_t KPerBlockAQ = KPerBlock / Problem::kQuantGroupSize;
 
         static_assert(std::is_same_v<AQLayout, ck_tile::tensor_layout::gemm::RowMajor>);
-        return GetAQGlobalVectorLoadSize<Problem, AQDataType, MPerBlock, KPerBlockAQ>();
+        return GetABQGlobalVectorLoadSize<Problem, AQDataType, MPerBlock, KPerBlockAQ>();
     }
 
     template <typename Problem>
diff --git a/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_base.hpp b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_base.hpp
new file mode 100644
index 0000000000..d900f78ff5
--- /dev/null
+++ b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_base.hpp
@@ -0,0 +1,53 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/ops/common.hpp"
+#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp"
+
+namespace ck_tile {
+
+template <typename Problem, typename Policy>
+struct GemmBQuantPipelineAgBgCrImplBase : public GemmPipelineAgBgCrImplBase<Problem, Policy>
+{
+    using Base           = GemmPipelineAgBgCrImplBase<Problem, Policy>;
+    using ADataType      = typename Base::ADataType;
+    using ALayout        = typename Base::ALayout;
+    using BDataType      = typename Base::BDataType;
+    using BLayout        = typename Base::BLayout;
+    using BlockGemmShape = typename Base::BlockGemmShape;
+
+    using BQLayout = remove_cvref_t<typename Problem::BQLayout>;
+
+    static constexpr index_t MPerBlock = BlockGemmShape::kM;
+    static constexpr index_t NPerBlock = BlockGemmShape::kN;
+    static constexpr index_t KPerBlock = BlockGemmShape::kK;
+
+    static constexpr index_t QuantGroupSize = Problem::kQuantGroupSize;
+    static constexpr index_t KPerBlockBQ    = KPerBlock / QuantGroupSize;
+
+    static_assert(KPerBlock % QuantGroupSize == 0,
+                  "KPerBlock must be a multiple of QuantGroupSize");
+
+    // Create DRAM tile window for BQ
+    template <typename BQDramBlockWindowTmp>
+    CK_TILE_DEVICE constexpr auto
+    GetBQDramLoadWindow(const BQDramBlockWindowTmp& bq_dram_block_window_tmp) const
+    {
+        static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
+
+        using YPerTile = number<NPerBlock>;
+        using XPerTile = number<KPerBlockBQ>;
+
+        auto bq_copy_dram_window =
+            make_tile_window(bq_dram_block_window_tmp.get_bottom_tensor_view(),
+                             make_tuple(YPerTile(), XPerTile()),
+                             bq_dram_block_window_tmp.get_window_origin(),
+                             Policy::template MakeBQDramTileDistribution<Problem>());
+        return bq_copy_dram_window;
+    }
+};
+
+} // namespace ck_tile
diff --git a/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_policy.hpp b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_policy.hpp
new file mode 100644
index 0000000000..a5ed83d24b
--- /dev/null
+++ b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_policy.hpp
@@ -0,0 +1,93 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/ops/gemm/pipeline/gemm_universal_pipeline_ag_bg_cr_policy.hpp"
+#include "gemm_group_quant_utils.hpp"
+
+namespace ck_tile {
+
+struct GemmBQuantPipelineAgBgCrDefaultPolicy : public UniversalGemmPipelineAgBgCrPolicy
+{
+    using Base = UniversalGemmPipelineAgBgCrPolicy;
+    using Base::I0;
+    using Base::I1;
+    using Base::I2;
+
+    using Base::ATileAccessPattern;
+    using Base::BTileAccessPattern;
+
+    template <typename Problem>
+    CK_TILE_HOST_DEVICE static constexpr auto GetVectorSizeBQ()
+    {
+        using BQLayout                = remove_cvref_t<typename Problem::BQLayout>;
+        using BQDataType              = remove_cvref_t<typename Problem::BQDataType>;
+        constexpr index_t NPerBlock   = Problem::BlockGemmShape::kN;
+        constexpr index_t KPerBlock   = Problem::BlockGemmShape::kK;
+        constexpr index_t KPerBlockBQ = KPerBlock / Problem::kQuantGroupSize;
+
+        static_assert(std::is_same_v<BQLayout, ck_tile::tensor_layout::gemm::ColumnMajor>);
+        return GetABQGlobalVectorLoadSize<Problem, BQDataType, NPerBlock, KPerBlockBQ>();
+    }
+
+    template <typename Problem>
+    CK_TILE_HOST_DEVICE static constexpr auto MakeBQDramTileDistribution()
+    {
+        using BQLayout       = remove_cvref_t<typename Problem::BQLayout>;
+        using BlockGemmShape = typename Problem::BlockGemmShape;
+
+        constexpr index_t BlockSize   = Problem::kBlockSize;
+        constexpr index_t NPerBlock   = Problem::BlockGemmShape::kN;
+        constexpr index_t KPerBlock   = Problem::BlockGemmShape::kK;
+        constexpr index_t KPerBlockBQ = KPerBlock / Problem::kQuantGroupSize;
+        constexpr index_t VecLoadSize = GetVectorSizeBQ<Problem>();
+        using WarpTile                = typename Problem::BlockGemmShape::WarpTile;
+        using WarpGemm                = WarpGemmDispatcher<typename Problem::ComputeDataType,
+                                                               typename Problem::ComputeDataType,
+                                                               typename Problem::CDataType,
+                                                               WarpTile::at(I0),
+                                                               WarpTile::at(I1),
+                                                               WarpTile::at(I2),
+                                                               Problem::TransposeC>;
+
+        static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
+        using TileEncodingPattern = TileDistributionEncodingPatternBQ<BlockGemmShape,
+                                                                      WarpGemm,
+                                                                      BlockSize,
+                                                                      NPerBlock,
+                                                                      KPerBlockBQ,
+                                                                      VecLoadSize>;
+
+        return TileEncodingPattern::Make2DStaticTileDistribution();
+    }
+
+    template <typename Problem>
+    CK_TILE_HOST_DEVICE static constexpr auto GetBlockGemm()
+    {
+        using BlockWarps = typename Problem::BlockGemmShape::BlockWarps;
+        using WarpTile   = typename Problem::BlockGemmShape::WarpTile;
+
+        static_assert(Problem::kQuantGroupSize % WarpTile::at(I2) == 0,
+                      "KPerWarpGemm must be a multiple of kQuantGroupSize!");
+
+        using WarpGemm = WarpGemmDispatcher<typename Problem::ComputeDataType,
+                                                typename Problem::ComputeDataType,
+                                                typename Problem::CDataType,
+                                                WarpTile::at(I0),
+                                                WarpTile::at(I1),
+                                                WarpTile::at(I2),
+                                                Problem::TransposeC>;
+        static_assert(std::is_same_v<typename Problem::ComputeDataType, fp8_t> ||
+                      std::is_same_v<typename Problem::ComputeDataType, bf8_t>);
+        static_assert(std::is_same_v<typename Problem::CDataType, float>);
+        using BlockGemmPolicy = BlockGemmASmemBSmemCRegV1CustomPolicy<typename Problem::ADataType,
+                                                                      typename Problem::BDataType,
+                                                                      typename Problem::CDataType,
+                                                                      BlockWarps,
+                                                                      WarpGemm>;
+        return BQuantBlockUniversalGemmAsBsCr<Problem, BlockGemmPolicy>{};
+    }
+};
+
+} // namespace ck_tile
diff --git a/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_v3.hpp b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_v3.hpp
new file mode 100644
index 0000000000..7ce6598b80
--- /dev/null
+++ b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_v3.hpp
@@ -0,0 +1,475 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <string>
+#include <sstream>
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/ops/gemm/pipeline/gemm_universal_pipeline_ag_bg_cr_policy.hpp"
+#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
+#include "ck_tile/ops/gemm_group_quant/pipeline/gemm_bquant_pipeline_ag_bg_cr_base.hpp"
+#include "ck_tile/host/concat.hpp"
+
+namespace ck_tile {
+
+// Compute optimized pipeline
+// GlobalPrefetchStages: 2
+// LocalPreFillStages: 1
+// LocalPreFetchStages: 1
+// LocalSharedMemoryBuffer: 1
+
+template <typename Problem>
+struct BaseBQuantGemmPipelineAgBgCrCompV3 : public BaseGemmPipelineAgBgCrCompV3<Problem>
+{
+    template <typename RunFunction>
+    CK_TILE_HOST_DEVICE static auto
+    TailHandler(const RunFunction& run_func, bool has_hot_loop, TailNumber tail_number)
+    {
+        if(has_hot_loop)
+        {
+            if(tail_number == ck_tile::TailNumber::Full)
+            {
+                return run_func(
+                    ck_tile::bool_constant<true>{},
+                    ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Full>{});
+            }
+            else if(tail_number == ck_tile::TailNumber::Odd)
+            {
+                return run_func(
+                    ck_tile::bool_constant<true>{},
+                    ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Odd>{});
+            }
+            else if(tail_number == ck_tile::TailNumber::Even)
+            {
+                return run_func(
+                    ck_tile::bool_constant<true>{},
+                    ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Even>{});
+            }
+            else
+            {
+                throw std::runtime_error("Unsupported tail number for this operation !!!");
+            }
+        }
+        else
+        {
+            if(tail_number == ck_tile::TailNumber::Full)
+            {
+                return run_func(
+                    ck_tile::bool_constant<false>{},
+                    ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Full>{});
+            }
+            else if(tail_number == ck_tile::TailNumber::Odd)
+            {
+                return run_func(
+                    ck_tile::bool_constant<false>{},
+                    ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Odd>{});
+            }
+            else if(tail_number == ck_tile::TailNumber::Even)
+            {
+                return run_func(
+                    ck_tile::bool_constant<false>{},
+                    ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Even>{});
+            }
+            else
+            {
+                throw std::runtime_error("Unsupported tail number for this operation !!!");
+            }
+        }
+    }
+};
+
+template <typename Problem, typename Policy = GemmBQuantPipelineAgBgCrDefaultPolicy>
+struct BQuantGemmPipelineAgBgCrCompV3 : public BaseBQuantGemmPipelineAgBgCrCompV3<Problem>
+{
+    using Base             = BaseGemmPipelineAgBgCrCompV3<Problem>;
+    using PipelineImplBase = GemmBQuantPipelineAgBgCrImplBase<Problem, Policy>;
+
+    using ADataType      = remove_cvref_t<typename Problem::ADataType>;
+    using BDataType      = remove_cvref_t<typename Problem::BDataType>;
+    using BQDataType     = remove_cvref_t<typename Problem::BQDataType>;
+    using CDataType      = remove_cvref_t<typename Problem::CDataType>;
+    using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
+
+    using I0 = number<0>;
+    using I1 = number<1>;
+    using I2 = number<2>;
+
+    static constexpr index_t APackedSize =
+        ck_tile::numeric_traits<remove_cvref_t<ADataType>>::PackedSize;
+    static constexpr index_t BPackedSize =
+        ck_tile::numeric_traits<remove_cvref_t<BDataType>>::PackedSize;
+
+    static constexpr index_t BQPackedSize =
+        ck_tile::numeric_traits<remove_cvref_t<BQDataType>>::PackedSize;
+
+    using ALayout  = remove_cvref_t<typename Problem::ALayout>;
+    using BQLayout = remove_cvref_t<typename Problem::BQLayout>;
+    using BLayout  = remove_cvref_t<typename Problem::BLayout>;
+    using CLayout  = remove_cvref_t<typename Problem::CLayout>;
+
+    using BlockGemm = remove_cvref_t<decltype(Policy::template GetBlockGemm<Problem>())>;
+
+    static constexpr index_t BlockSize      = Problem::kBlockSize;
+    static constexpr index_t MPerBlock      = BlockGemmShape::kM;
+    static constexpr index_t NPerBlock      = BlockGemmShape::kN;
+    static constexpr index_t KPerBlock      = BlockGemmShape::kK;
+    static constexpr index_t QuantGroupSize = Problem::kQuantGroupSize;
+    static constexpr index_t KPerBlockBQ    = BlockGemmShape::kK / QuantGroupSize;
+
+    static constexpr index_t GetVectorSizeA() { return Policy::template GetVectorSizeA<Problem>(); }
+    static constexpr index_t GetVectorSizeB() { return Policy::template GetVectorSizeB<Problem>(); }
+    static constexpr index_t GetVectorSizeC() { return Policy::template GetVectorSizeC<Problem>(); }
+    static constexpr index_t GetVectorSizeBQ()
+    {
+        return Policy::template GetVectorSizeBQ<Problem>();
+    }
+
+    static constexpr index_t GetSmemPackA() { return Policy::template GetSmemPackA<Problem>(); }
+    static constexpr index_t GetSmemPackB() { return Policy::template GetSmemPackB<Problem>(); }
+
+    static constexpr bool kPadM = Problem::kPadM;
+    static constexpr bool kPadN = Problem::kPadN;
+    static constexpr bool kPadK = Problem::kPadK;
+
+    static constexpr bool DoubleSmemBuffer = Problem::DoubleSmemBuffer;
+
+    static constexpr bool HasHotLoop = Problem::HasHotLoop;
+    static constexpr auto TailNum    = Problem::TailNum;
+    static constexpr auto Scheduler  = Problem::Scheduler;
+
+    using Base::PrefetchStages;
+
+    [[nodiscard]] CK_TILE_HOST static const std::string GetName()
+    {
+        // clang-format off
+        constexpr index_t WaveNumM = BlockGemmShape::BlockWarps::at(I0{});
+        constexpr index_t WaveNumN = BlockGemmShape::BlockWarps::at(I1{});
+        return concat('_', "bquant_pipeline_AgBgCrCompV3",
+                      concat('x', MPerBlock, NPerBlock, KPerBlock),
+                      BlockSize,
+                      concat('x', WaveNumM, WaveNumN),
+                      concat('x', BlockGemm::WarpGemm::kM, BlockGemm::WarpGemm::kN, BlockGemm::WarpGemm::kK),
+                      concat('x', kPadM, kPadN, kPadK), "QuantGroupSize", QuantGroupSize);
+        // clang-format on
+    }
+
+    CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
+    {
+        return Policy::template GetSmemSize<Problem>();
+    }
+
+    CK_TILE_HOST static std::string Print()
+    {
+        constexpr index_t MPerXDL = BlockGemm::WarpGemm::kM;
+        constexpr index_t NPerXDL = BlockGemm::WarpGemm::kN;
+        constexpr index_t KPerXDL = BlockGemm::WarpGemm::WarpGemmAttribute::Impl::kK;
+
+        constexpr index_t WaveSize = 64;
+        constexpr index_t WaveNumM = BlockGemmShape::BlockWarps::at(I0{});
+        constexpr index_t WaveNumN = BlockGemmShape::BlockWarps::at(I1{});
+
+        constexpr index_t A_LDS_Read_Width = GetSmemPackA();
+        constexpr index_t B_LDS_Read_Width = GetSmemPackB();
+
+        constexpr index_t A_LDS_Write_Width = GetSmemPackA();
+        constexpr index_t B_LDS_Write_Width = GetSmemPackB();
+
+        constexpr index_t A_Buffer_Load_Inst_Num =
+            MPerBlock * KPerBlock / (BlockSize * GetVectorSizeA());
+        constexpr index_t B_Buffer_Load_Inst_Num =
+            NPerBlock * KPerBlock / (BlockSize * GetVectorSizeB());
+        constexpr index_t BQ_Buffer_Load_Inst_Num =
+            NPerBlock * KPerBlockBQ / (BlockSize * GetVectorSizeBQ());
+
+        constexpr index_t A_LDS_Write_Inst_Num =
+            MPerBlock * KPerBlock / (BlockSize * A_LDS_Write_Width);
+        constexpr index_t B_LDS_Write_Inst_Num =
+            NPerBlock * KPerBlock / (BlockSize * B_LDS_Write_Width);
+
+        constexpr index_t A_LDS_Read_Inst_Num =
+            WaveNumN * MPerBlock * KPerBlock / (BlockSize * A_LDS_Read_Width);
+        constexpr index_t B_LDS_Read_Inst_Num =
+            WaveNumM * NPerBlock * KPerBlock / (BlockSize * B_LDS_Read_Width);
+
+        constexpr index_t C_MFMA_Inst_Num = MPerBlock * NPerBlock * KPerBlock /
+                                            (BlockSize / WaveSize) / (MPerXDL * NPerXDL * KPerXDL);
+
+        auto str = std::stringstream{};
+
+        str << "A/B vector size: " << GetVectorSizeA() << ", " << GetVectorSizeB() << ", "
+            << "BQ vector size: " << GetVectorSizeBQ() << "\n"
+            << "A/B LDS read/write width: " << A_LDS_Read_Width << ", " << B_LDS_Read_Width << "\n"
+            << "A/B buffer load inst: " << A_Buffer_Load_Inst_Num << ", " << B_Buffer_Load_Inst_Num
+            << ", " << "BQ buffer load inst: " << BQ_Buffer_Load_Inst_Num << "\n"
+            << "A/B LDS write inst: " << A_LDS_Write_Inst_Num << ", " << B_LDS_Write_Inst_Num
+            << "\n"
+            << "A/B LDS read inst: " << A_LDS_Read_Inst_Num << ", " << B_LDS_Read_Inst_Num << "\n"
+            << "C MFMA inst: " << C_MFMA_Inst_Num << "\n"
+            << "QuantGroupSize: " << QuantGroupSize << "\n"
+            << "KPack: " << BlockGemm::Traits::KPack << "\n"
+            << "PrefetchStages: " << PrefetchStages << "\n";
+        return str.str();
+    }
+
+    template <GemmPipelineScheduler Scheduler>
+    struct PipelineImpl : public PipelineImplBase
+    {
+    };
+
+    template <>
+    struct PipelineImpl<GemmPipelineScheduler::Intrawave> : public PipelineImplBase
+    {
+        using Base = PipelineImplBase;
+
+        template <bool HasHotLoop,
+                  TailNumber TailNum,
+                  typename ADramBlockWindowTmp,
+                  typename BDramBlockWindowTmp,
+                  typename BQDramBlockWindowTmp,
+                  typename AElementFunction,
+                  typename BElementFunction>
+        CK_TILE_DEVICE auto operator()(const ADramBlockWindowTmp& a_dram_block_window_tmp,
+                                       const AElementFunction& a_element_func,
+                                       const BDramBlockWindowTmp& b_dram_block_window_tmp,
+                                       const BElementFunction& b_element_func,
+                                       const BQDramBlockWindowTmp& bq_dram_block_window_tmp,
+                                       index_t num_loop,
+                                       void* p_smem) const
+        {
+            static_assert(
+                std::is_same_v<ADataType, remove_cvref_t<typename ADramBlockWindowTmp::DataType>> &&
+                    std::is_same_v<BDataType,
+                                   remove_cvref_t<typename BDramBlockWindowTmp::DataType>> &&
+                    std::is_same_v<BQDataType,
+                                   remove_cvref_t<typename BQDramBlockWindowTmp::DataType>>,
+                "A/B/BQ Dram block window should have the same data type as appropriate "
+                "([A|B|BQ]DataType) defined in Problem definition!");
+
+            constexpr bool is_a_col_major =
+                std::is_same_v<ALayout, tensor_layout::gemm::ColumnMajor>;
+            constexpr bool is_bq_col_major =
+                std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>;
+            constexpr bool is_b_row_major = std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>;
+
+            static_assert(is_bq_col_major, "Bq must be col major (row major not supported yet)");
+            static_assert(NPerBlock == BQDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
+                              KPerBlockBQ == BQDramBlockWindowTmp{}.get_window_lengths()[I1{}],
+                          "Bq block window has incorrect lengths for defined BqLayout!");
+
+            static_assert(is_a_col_major
+                              ? (KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
+                                 MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}])
+                              : (MPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
+                                 KPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[I1{}]),
+                          "A block window has incorrect lengths for defined ALayout!");
+            static_assert(is_b_row_major
+                              ? (KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
+                                 NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}])
+                              : (NPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I0{}] &&
+                                 KPerBlock == BDramBlockWindowTmp{}.get_window_lengths()[I1{}]),
+                          "B block window has incorrect lengths for defined BLayout!");
+
+            using ADramTileWindowStep  = typename ADramBlockWindowTmp::BottomTensorIndex;
+            using BDramTileWindowStep  = typename BDramBlockWindowTmp::BottomTensorIndex;
+            using BQDramTileWindowStep = typename BQDramBlockWindowTmp::BottomTensorIndex;
+
+            auto&& [a_lds_block, b_lds_block] = Base::GetABLdsTensorViews(p_smem);
+
+            constexpr auto a_lds_load_tile_distr =
+                make_static_tile_distribution(BlockGemm::MakeABlockDistributionEncode());
+            constexpr auto b_lds_load_tile_distr =
+                make_static_tile_distribution(BlockGemm::MakeBBlockDistributionEncode());
+
+            auto&& [a_copy_dram_window, a_copy_lds_window, a_lds_gemm_window] =
+                Base::GetAWindows(a_dram_block_window_tmp, a_lds_block, a_lds_load_tile_distr);
+            auto&& [b_copy_dram_window, b_copy_lds_window, b_lds_gemm_window] =
+                Base::GetBWindows(b_dram_block_window_tmp, b_lds_block, b_lds_load_tile_distr);
+            auto bq_copy_dram_window = Base::GetBQDramLoadWindow(bq_dram_block_window_tmp);
+
+            using ABlockTileDistr  = decltype(a_copy_dram_window.get_tile_distribution());
+            using BBlockTileDistr  = decltype(b_copy_dram_window.get_tile_distribution());
+            using BQBlockTileDistr = decltype(bq_copy_dram_window.get_tile_distribution());
+
+            using ABlockTile =
+                decltype(make_static_distributed_tensor<ADataType>(ABlockTileDistr{}));
+            using BBlockTile =
+                decltype(make_static_distributed_tensor<BDataType>(BBlockTileDistr{}));
+            using BQBlockTile =
+                decltype(make_static_distributed_tensor<BQDataType>(BQBlockTileDistr{}));
+
+            auto block_gemm = BlockGemm();
+
+            ABlockTile a_block_tile;
+            BBlockTile b_block_tile;
+            BQBlockTile bq_block_tile[2];
+            int currIdx = 0;
+
+            auto c_block_tile = block_gemm.MakeCBlockTile();
+
+            constexpr ADramTileWindowStep a_dram_tile_window_step =
+                is_a_col_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
+            constexpr BDramTileWindowStep b_dram_tile_window_step =
+                is_b_row_major ? make_array(KPerBlock, 0) : make_array(0, KPerBlock);
+            constexpr BQDramTileWindowStep bq_dram_tile_window_step =
+                is_bq_col_major ? make_array(0, KPerBlockBQ) : make_array(KPerBlockBQ, 0);
+
+            // DRAM prefetch (global read 0)
+            Base::GlobalPrefetch(a_block_tile, a_copy_dram_window, a_dram_tile_window_step);
+            Base::GlobalPrefetch(b_block_tile, b_copy_dram_window, b_dram_tile_window_step);
+            Base::GlobalPrefetch(
+                bq_block_tile[currIdx], bq_copy_dram_window, bq_dram_tile_window_step);
+
+            tile_elementwise_inout([](auto& c) { c = 0; }, c_block_tile);
+
+            if constexpr(is_a_col_major)
+            {
+                auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
+                    Policy::template MakeShuffled2DStaticTileDistribution<Problem>());
+                transpose_tile2d(a_shuffle_tmp, a_block_tile);
+                Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
+            }
+            else
+            {
+                Base::LocalPrefill(a_copy_lds_window, a_block_tile, a_element_func);
+            }
+
+            if constexpr(is_b_row_major)
+            {
+                auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
+                    Policy::template MakeShuffled2DStaticTileDistribution<Problem>());
+                transpose_tile2d(b_shuffle_tmp, b_block_tile);
+                Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
+            }
+            else
+            {
+                Base::LocalPrefill(b_copy_lds_window, b_block_tile, b_element_func);
+            }
+
+            Base::GlobalPrefetch(a_block_tile, a_copy_dram_window, a_dram_tile_window_step);
+            Base::GlobalPrefetch(b_block_tile, b_copy_dram_window, b_dram_tile_window_step);
+
+            block_sync_lds();
+
+            block_gemm.LocalPrefetch(a_lds_gemm_window, b_lds_gemm_window);
+
+            __builtin_amdgcn_sched_barrier(0);
+
+            if constexpr(HasHotLoop)
+            {
+                index_t i = 0;
+                do
+                {
+                    block_sync_lds();
+
+                    if constexpr(is_a_col_major)
+                    {
+                        auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
+                            Policy::template MakeShuffledARegTileDistribution<Problem>());
+                        transpose_tile2d(a_shuffle_tmp, a_block_tile);
+                        Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
+                    }
+                    else
+                    {
+                        Base::LocalPrefill(a_copy_lds_window, a_block_tile, a_element_func);
+                    }
+                    if constexpr(is_b_row_major)
+                    {
+                        auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
+                            Policy::template MakeShuffledBRegTileDistribution<Problem>());
+                        transpose_tile2d(b_shuffle_tmp, b_block_tile);
+                        Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
+                    }
+                    else
+                    {
+                        Base::LocalPrefill(b_copy_lds_window, b_block_tile, b_element_func);
+                    }
+
+                    Base::GlobalPrefetch(a_block_tile, a_copy_dram_window, a_dram_tile_window_step);
+                    Base::GlobalPrefetch(b_block_tile, b_copy_dram_window, b_dram_tile_window_step);
+                    Base::GlobalPrefetch(bq_block_tile[(currIdx + 1) % 2],
+                                         bq_copy_dram_window,
+                                         bq_dram_tile_window_step);
+
+                    block_gemm(
+                        c_block_tile, bq_block_tile[currIdx], a_lds_gemm_window, b_lds_gemm_window);
+
+                    currIdx = (currIdx + 1) % 2;
+
+                    block_sync_lds();
+
+                    block_gemm.LocalPrefetch(a_lds_gemm_window, b_lds_gemm_window);
+                    __builtin_amdgcn_sched_barrier(0);
+
+                    i += 1;
+                } while(i < (num_loop - 1));
+            }
+            // tail
+            if constexpr((TailNum == TailNumber::Full) || (TailNum == TailNumber::Odd))
+            {
+                block_gemm(
+                    c_block_tile, bq_block_tile[currIdx], a_lds_gemm_window, b_lds_gemm_window);
+            }
+            else
+            {
+                Base::GlobalPrefetch(bq_block_tile[(currIdx + 1) % 2],
+                                     bq_copy_dram_window,
+                                     bq_dram_tile_window_step);
+                block_gemm(
+                    c_block_tile, bq_block_tile[currIdx], a_lds_gemm_window, b_lds_gemm_window);
+                block_sync_lds();
+
+                currIdx = (currIdx + 1) % 2;
+
+                if constexpr(is_a_col_major)
+                {
+                    auto a_shuffle_tmp = make_static_distributed_tensor<ADataType>(
+                        Policy::template MakeShuffledARegTileDistribution<Problem>());
+                    transpose_tile2d(a_shuffle_tmp, a_block_tile);
+                    Base::LocalPrefill(a_copy_lds_window, a_shuffle_tmp, a_element_func);
+                }
+                else
+                {
+                    Base::LocalPrefill(a_copy_lds_window, a_block_tile, a_element_func);
+                }
+                if constexpr(is_b_row_major)
+                {
+                    auto b_shuffle_tmp = make_static_distributed_tensor<BDataType>(
+                        Policy::template MakeShuffledBRegTileDistribution<Problem>());
+                    transpose_tile2d(b_shuffle_tmp, b_block_tile);
+                    Base::LocalPrefill(b_copy_lds_window, b_shuffle_tmp, b_element_func);
+                }
+                else
+                {
+                    Base::LocalPrefill(b_copy_lds_window, b_block_tile, b_element_func);
+                }
+                block_sync_lds();
+                block_gemm.LocalPrefetch(a_lds_gemm_window, b_lds_gemm_window);
+                block_gemm(
+                    c_block_tile, bq_block_tile[currIdx], a_lds_gemm_window, b_lds_gemm_window);
+            }
+            return c_block_tile;
+        }
+    };
+    template <typename ADramBlockWindowTmp,
+              typename BDramBlockWindowTmp,
+              typename BQDramBlockWindowTmp>
+    CK_TILE_DEVICE auto operator()(const ADramBlockWindowTmp& a_dram_block_window_tmp,
+                                   const BDramBlockWindowTmp& b_dram_block_window_tmp,
+                                   const BQDramBlockWindowTmp& bq_dram_block_window_tmp,
+                                   index_t num_loop,
+                                   void* p_smem) const
+    {
+        return PipelineImpl<Scheduler>{}.template operator()<HasHotLoop, TailNum>(
+            a_dram_block_window_tmp,
+            [](const ADataType& a) { return a; },
+            b_dram_block_window_tmp,
+            [](const BDataType& b) { return b; },
+            bq_dram_block_window_tmp,
+            num_loop,
+            p_smem);
+    }
+};
+
+} // namespace ck_tile
diff --git a/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_group_quant_utils.hpp b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_group_quant_utils.hpp
index aa878b27fa..56a906a6bc 100644
--- a/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_group_quant_utils.hpp
+++ b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_group_quant_utils.hpp
@@ -8,7 +8,7 @@
 namespace ck_tile {
 
 template <typename Problem, typename DataType, index_t YPerTile, index_t XPerTile>
-CK_TILE_HOST_DEVICE static constexpr auto GetAQGlobalVectorLoadSize()
+CK_TILE_HOST_DEVICE static constexpr auto GetABQGlobalVectorLoadSize()
 {
     using I1                 = number<1>;
     constexpr index_t NWarps = Problem::BlockGemmShape::BlockWarps::at(I1{});
@@ -164,4 +164,56 @@ struct TileDistributionEncodingPatternAQTransposedC : public TileDistributionEnc
     }
 };
 
+// TODO:: might need to update
+template <typename BlockGemmShape,
+          typename WarpGemm,
+          index_t BlockSize,
+          index_t YPerTile,
+          index_t XPerTile,
+          index_t VecSize>
+struct TileDistributionEncodingPatternBQ : public TileDistributionEncodingPattern
+{
+    // TODO: make pattern where below condition does not need to hold - GGemmMultiDSplitk!
+    static_assert(XPerTile % VecSize == 0, "XPerTile must be a multiple of VecSize!");
+    static constexpr index_t warp_size = get_warp_size();
+    static constexpr index_t num_warps = BlockSize / get_warp_size();
+
+    static constexpr index_t MWarps = BlockGemmShape::BlockWarps::at(number<0>{});
+    static constexpr index_t NWarps = BlockGemmShape::BlockWarps::at(number<1>{});
+    static constexpr index_t KWarps = BlockGemmShape::BlockWarps::at(number<2>{});
+
+    static constexpr index_t NIterPerWarp = BlockGemmShape::kN / (NWarps * WarpGemm::kN);
+
+    static_assert(num_warps == MWarps * NWarps * KWarps);
+
+    // KWarps > 1 isn't supported
+    static_assert(KWarps == 1);
+
+    // # of elements per thread
+    static constexpr index_t X  = XPerTile;
+    static constexpr index_t XR = 2;
+
+    // Number of iters per warp
+    // MIters are indexed using (Y0, Y1)
+    static constexpr index_t Y0 = NIterPerWarp;
+
+    // # of warps in Y dim
+    static constexpr index_t Y1 = NWarps;
+
+    static constexpr index_t Y2 = WarpGemm::kN;
+
+    static_assert(Y0 * Y1 * Y2 == YPerTile, "Y0, Y1, Y2 must cover the blocktile along Y.");
+
+    CK_TILE_HOST_DEVICE static constexpr auto Make2DStaticTileDistribution()
+    {
+        return make_static_tile_distribution(
+            tile_distribution_encoding<sequence<MWarps, XR>,
+                                       tuple<sequence<Y0, Y1, Y2>, sequence<X>>,
+                                       tuple<sequence<0, 1>, sequence<0, 1>>,
+                                       tuple<sequence<0, 1>, sequence<1, 2>>,
+                                       sequence<1, 2>,
+                                       sequence<0, 0>>{});
+    }
+};
+
 } // namespace ck_tile
diff --git a/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_problem.hpp b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_quant_pipeline_problem.hpp
similarity index 53%
rename from include/ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_problem.hpp
rename to include/ck_tile/ops/gemm_group_quant/pipeline/gemm_quant_pipeline_problem.hpp
index dfad7ba83d..69b8cd901e 100644
--- a/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_aquant_pipeline_problem.hpp
+++ b/include/ck_tile/ops/gemm_group_quant/pipeline/gemm_quant_pipeline_problem.hpp
@@ -121,4 +121,107 @@ using GemmAQuantPipelineProblem = GemmAQuantPipelineProblemBase<ADataType_,
                                                                 HasHotLoop_,
                                                                 TailNum_>;
 
+template <typename ADataType_,
+          typename BDataType_,
+          typename BQDataType_,
+          typename CDataType_,
+          typename BlockGemmShape_,
+          typename Traits_,
+          uint32_t QuantGroupSize_,
+          typename ComputeDataType_        = 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(); }();
+};
+
+template <typename ADataType_,
+          typename BDataType_,
+          typename BQDataType_,
+          typename CDataType_,
+          typename BlockGemmShape_,
+          typename Traits_,
+          uint32_t QuantGroupSize_,
+          typename ComputeDataType_        = ADataType_,
+          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_>;
+
 } // namespace ck_tile
diff --git a/include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_aquant_traits.hpp b/include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_quant_traits.hpp
similarity index 52%
rename from include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_aquant_traits.hpp
rename to include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_quant_traits.hpp
index fe96c28f33..a156bb773d 100644
--- a/include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_aquant_traits.hpp
+++ b/include/ck_tile/ops/gemm_group_quant/pipeline/tile_gemm_quant_traits.hpp
@@ -28,6 +28,35 @@ struct TileGemmAQuantTraits
     using CLayout  = CLayout_;
     using AQLayout = AQLayout_;
 
+    static constexpr bool TransposeC            = false;
+    static constexpr bool UseStructuredSparsity = false;
+    static constexpr index_t NumWaveGroups      = 1;
+
+    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_;