diff --git a/test/ck_tile/multiple_d_gemm/CMakeLists.txt b/test/ck_tile/multiple_d_gemm/CMakeLists.txt
new file mode 100644
index 0000000000..ef2289cba9
--- /dev/null
+++ b/test/ck_tile/multiple_d_gemm/CMakeLists.txt
@@ -0,0 +1,4 @@
+# Currently ck_tile is only built on gfx9
+if(GPU_TARGETS MATCHES "gfx9")
+    add_gtest_executable(test_ck_tile_multiple_d_gemm test_multiple_d_gemm.cpp)
+endif()
diff --git a/test/ck_tile/multiple_d_gemm/test_multiple_d_gemm.cpp b/test/ck_tile/multiple_d_gemm/test_multiple_d_gemm.cpp
new file mode 100644
index 0000000000..9bcc64e930
--- /dev/null
+++ b/test/ck_tile/multiple_d_gemm/test_multiple_d_gemm.cpp
@@ -0,0 +1,26 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <tuple>
+
+#include "gtest/gtest.h"
+
+#include "ck_tile/host.hpp"
+#include "test_multiple_d_gemm_util.hpp"
+
+using F16 = ck_tile::half_t;
+using F32 = float;
+
+using Row = ck_tile::tensor_layout::gemm::RowMajor;
+using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
+
+// clang-format off
+using KernelTypes = ::testing::Types<
+    //          ALayout, BLayout, CLayout, D0Layout, D1Layout, ADataType, BDataType, D0DataType,  D0DataType, AccDataType, CDataType
+    std::tuple<    Row,     Col,     Row,     Row,      Row,      F16,       F16,          F16,        F16,        F32,      F16>
+    >;
+// clang-format on
+
+TYPED_TEST_SUITE(TestCkTileMultipleDGemm, KernelTypes);
+
+#include "test_multiple_d_gemm_ut_cases.inc"
diff --git a/test/ck_tile/multiple_d_gemm/test_multiple_d_gemm_ut_cases.inc b/test/ck_tile/multiple_d_gemm/test_multiple_d_gemm_ut_cases.inc
new file mode 100644
index 0000000000..fb3263a585
--- /dev/null
+++ b/test/ck_tile/multiple_d_gemm/test_multiple_d_gemm_ut_cases.inc
@@ -0,0 +1,9 @@
+#pragma once
+
+TYPED_TEST(TestCkTileMultipleDGemm, Basic)
+{
+    constexpr int M = 3840;
+    constexpr int N = 4096;
+    constexpr int K = 4096;
+    this->Run(M, N, K);
+}
diff --git a/test/ck_tile/multiple_d_gemm/test_multiple_d_gemm_util.hpp b/test/ck_tile/multiple_d_gemm/test_multiple_d_gemm_util.hpp
new file mode 100644
index 0000000000..4b526620c1
--- /dev/null
+++ b/test/ck_tile/multiple_d_gemm/test_multiple_d_gemm_util.hpp
@@ -0,0 +1,352 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+#pragma once
+
+#include <sstream>
+#include <gtest/gtest.h>
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/host.hpp"
+#include "ck_tile/host/kernel_launch.hpp"
+#include "ck_tile/ops/epilogue.hpp"
+#include "ck_tile/ops/gemm.hpp"
+#include "ck_tile/ops/gemm/kernel/gemm_kernel.hpp"
+#include "ck_tile/ops/elementwise/unary_element_wise_operation.hpp"
+
+template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
+auto calculate_rtol_atol(const ck_tile::index_t K,
+                         const ck_tile::index_t kbatch,
+                         const float max_accumulated_value)
+{
+    using ComputeType =
+        std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
+    // Calculate thresholds
+    const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
+        ck_tile::integer_divide_ceil(K, kbatch));
+    const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
+        max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
+    // Calculate error due to split_k accumulation
+    const auto rtol_split_k =
+        ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
+    const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
+        max_accumulated_value, kbatch);
+    // Use higher threshold
+    return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
+}
+
+template <typename Tuple>
+class TestCkTileMultipleDGemm : public ::testing::Test
+{
+    protected:
+    using ALayout     = std::tuple_element_t<0, Tuple>;
+    using BLayout     = std::tuple_element_t<1, Tuple>;
+    using D0Layout     = std::tuple_element_t<2, Tuple>;
+    using D1Layout     = std::tuple_element_t<3, Tuple>;
+    using CLayout     = std::tuple_element_t<4, Tuple>;
+    using ADataType   = std::tuple_element_t<5, Tuple>;
+    using BDataType   = std::tuple_element_t<6, Tuple>;
+    using D0DataType   = std::tuple_element_t<7, Tuple>;
+    using D1DataType   = std::tuple_element_t<8, Tuple>;
+    using AccDataType = std::tuple_element_t<9, Tuple>;
+    using CDataType   = std::tuple_element_t<10, Tuple>;
+    using DsLayout    = ck_tile::tuple<D0Layout, D1Layout>;
+    using DsDataType  = ck_tile::tuple<D0DataType, D1DataType>;
+
+    using CDEElementWiseFn = ck_tile::element_wise::ElementWiseAdd;
+
+    template <typename ADataType,
+          typename BDataType,
+          typename DsDataType,
+          typename AccDataType,
+          typename CDataType,
+          typename ALayout,
+          typename BLayout,
+          typename DsLayout,
+          typename CLayout,
+          typename CDEElementWise = ck_tile::element_wise::PassThrough>
+    void invoke_multi_d_gemm(const ck_tile::GemmHostArgs<DsDataType::size()>& args,
+                             const ck_tile::stream_config& s)
+    {
+        constexpr ck_tile::index_t M_Tile = 256;
+        constexpr ck_tile::index_t N_Tile = 256;
+        constexpr ck_tile::index_t K_Tile = 64;
+
+        constexpr ck_tile::index_t M_Warp = 2;
+        constexpr ck_tile::index_t N_Warp = 2;
+        constexpr ck_tile::index_t K_Warp = 1;
+
+        constexpr ck_tile::index_t M_Warp_Tile = 32;
+        constexpr ck_tile::index_t N_Warp_Tile = 32;
+        constexpr ck_tile::index_t K_Warp_Tile = 16;
+
+        constexpr bool DoubleSmemBuffer = false;
+
+        constexpr bool kPadM = false;
+        constexpr bool kPadN = false;
+        constexpr bool kPadK = false;
+
+        constexpr bool TransposeC = false;
+
+        constexpr int kBlockPerCu                         = 1;
+        constexpr ck_tile::index_t TileParitionerGroupNum = 8;
+        constexpr ck_tile::index_t TileParitionerM01      = 4;
+
+        using GemmShape =
+            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::
+            GemmSpatiallyLocalTilePartitioner<GemmShape, TileParitionerGroupNum, TileParitionerM01>;
+
+        using Traits = ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
+        using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<kPadM,
+                                                                     kPadN,
+                                                                     kPadK,
+                                                                     DoubleSmemBuffer,
+                                                                     ALayout,
+                                                                     BLayout,
+                                                                     CLayout,
+                                                                     TransposeC>;
+        using GemmPipelineProblem =
+            ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;
+
+        using BaseGemmPipeline = ck_tile::BaseGemmPipelineAgBgCrCompV3<GemmPipelineProblem>;
+
+        const ck_tile::index_t k_grain     = args.k_batch * K_Tile;
+        const ck_tile::index_t K_split     = (args.K + k_grain - 1) / k_grain * 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);
+
+        float ave_time{0};
+
+        const auto Run = [&](const auto has_hot_loop_, const auto tail_number_) {
+            constexpr bool has_hot_loop_v = has_hot_loop_.value;
+            constexpr auto tail_number_v  = tail_number_.value;
+            constexpr auto scheduler      = ck_tile::GemmPipelineScheduler::Intrawave;
+
+            using UniversalGemmProblem = ck_tile::UniversalGemmPipelineProblem<ADataType,
+                                                                               BDataType,
+                                                                               AccDataType,
+                                                                               GemmShape,
+                                                                               GemmUniversalTraits,
+                                                                               scheduler,
+                                                                               has_hot_loop_v,
+                                                                               tail_number_v>;
+
+            using GemmPipeline = ck_tile::GemmPipelineAgBgCrCompV3<UniversalGemmProblem>;
+            using GemmEpilogue = ck_tile::CShuffleEpilogue<
+                ck_tile::CShuffleEpilogueProblem<ADataType,
+                                                 BDataType,
+                                                 DsDataType,
+                                                 AccDataType,
+                                                 CDataType,
+                                                 DsLayout,
+                                                 CLayout,
+                                                 CDEElementWise,
+                                                 GemmPipelineProblem::kBlockSize,
+                                                 TilePartitioner::MPerBlock,
+                                                 TilePartitioner::NPerBlock,
+                                                 M_Warp,
+                                                 N_Warp,
+                                                 M_Warp_Tile,
+                                                 N_Warp_Tile,
+                                                 K_Warp_Tile,
+                                                 UniversalGemmProblem::TransposeC>>;
+            using Kernel = ck_tile::GemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
+            auto kargs   = Kernel::MakeKernelArgs(args);
+
+            const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch);
+            constexpr dim3 blocks = Kernel::BlockSize();
+
+            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: " << GemmShape::GetName() << '\n'
+                          << "problem: " << GemmPipelineProblem::GetName() << '\n'
+                          << "pipeline: " << GemmPipeline::GetName() << '\n'
+                          << "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
+                          << ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z
+                          << "}" << std::endl;
+            }
+
+            ave_time = ck_tile::launch_kernel(
+                s, ck_tile::make_kernel<blocks.x, kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
+            return ave_time;
+        };
+
+        if(has_hot_loop)
+        {
+            if(tail_num == ck_tile::TailNumber::Full)
+            {
+                Run(ck_tile::bool_constant<true>{},
+                    ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Full>{});
+            }
+            else
+            {
+                std::ostringstream err;
+                err << "For compute pipeline tail number should always be Full, but have \""
+                    << tail_num << "\" which is not supported! PrefetchStages: "
+                    << BaseGemmPipeline::PrefetchStages << "\n File: " << __FILE__ << ":"
+                    << __LINE__ << ", in function: " << __func__;
+                throw std::runtime_error(err.str());
+            }
+        }
+        else
+        {
+            std::ostringstream err;
+            err << "Num K loop must be larger than number of prefetech stages."
+                << "\n PrefetchStages: " << BaseGemmPipeline::PrefetchStages
+                << "\n File: " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
+            throw std::runtime_error(err.str());
+        }
+    }
+
+    public:
+    void Run(const int M,
+             const int N,
+             const int K,
+             int StrideA            = 0,
+             int StrideB            = 0,
+             int StrideD0            = 0,
+             int StrideD1            = 0,
+             int StrideC            = 0)
+    {
+        using namespace ck_tile::literals;
+
+        auto f_host_tensor_descriptor = [](std::size_t row,
+                                           std::size_t col,
+                                           std::size_t stride,
+                                           auto layout) {
+            if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
+            {
+                return ck_tile::HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return ck_tile::HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+        auto f_get_default_stride =
+            [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+                if(stride == 0)
+                {
+                    if constexpr(std::is_same_v<decltype(layout),
+                                                ck_tile::tensor_layout::gemm::RowMajor>)
+                    {
+                        return col;
+                    }
+                    else
+                    {
+                        return row;
+                    }
+                }
+                else
+                    return stride;
+            };
+
+
+            StrideA  = f_get_default_stride(M, N, StrideA, ALayout{});
+            StrideB  = f_get_default_stride(K, N, StrideB, BLayout{});
+            StrideD0 = f_get_default_stride(M, N, StrideD0, D0Layout{});
+            StrideD1 = f_get_default_stride(M, N, StrideD1, D1Layout{});
+            StrideC  = f_get_default_stride(M, N, StrideC, CLayout{});
+
+        ck_tile::HostTensor<ADataType> a_m_k_tesnor(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+        ck_tile::HostTensor<BDataType> b_k_n_tensors(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+        ck_tile::HostTensor<D0DataType> d0_m_n_tensors(f_host_tensor_descriptor(M, N, StrideD0, D0Layout{}));
+        ck_tile::HostTensor<D1DataType> d1_m_n_tensors(f_host_tensor_descriptor(M, N, StrideD1, D1Layout{}));
+        ck_tile::HostTensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+
+        ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k_tesnor);
+        ck_tile::FillUniformDistribution<BDataType>{-5.f, 5.f}(b_k_n_tensors);
+        ck_tile::FillUniformDistribution<D0DataType>{-1.f, 1.f}(d0_m_n_tensors);
+        ck_tile::FillUniformDistribution<D1DataType>{-1.f, 1.f}(d1_m_n_tensors);
+
+        ck_tile::DeviceMem a_m_k_dev_buf(a_m_k_tesnor.get_element_space_size_in_bytes());
+        ck_tile::DeviceMem b_k_n_dev_buf(b_k_n_tensors.get_element_space_size_in_bytes());
+        ck_tile::DeviceMem d0_m_n_dev_buf(d0_m_n_tensors.get_element_space_size_in_bytes());
+        ck_tile::DeviceMem d1_m_n_dev_buf(d1_m_n_tensors.get_element_space_size_in_bytes());
+        ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_device_result.get_element_space_size_in_bytes());
+
+        a_m_k_dev_buf.ToDevice(a_m_k_tesnor.mData.data());
+        b_k_n_dev_buf.ToDevice(b_k_n_tensors.mData.data());
+        d0_m_n_dev_buf.ToDevice(d0_m_n_tensors.mData.data());
+        d1_m_n_dev_buf.ToDevice(d1_m_n_tensors.mData.data());
+    
+        c_m_n_dev_buf.SetZero();
+        c_m_n_device_result.SetZero();
+
+        std::array<const void*, DsDataType::size()> ds_ptr_buf = {d0_m_n_dev_buf.GetDeviceBuffer(),
+            d1_m_n_dev_buf.GetDeviceBuffer()};
+        std::array<ck_tile::index_t, DsDataType::size()> stridesDs = {StrideD0, StrideD1};
+        
+        ck_tile::GemmHostArgs<DsDataType::size()> args({
+            a_m_k_dev_buf.GetDeviceBuffer(),
+            b_k_n_dev_buf.GetDeviceBuffer(),
+            ds_ptr_buf,
+            c_m_n_dev_buf.GetDeviceBuffer(),
+            /* kBatch */ 1,
+            M,
+            N,
+            K,
+            StrideA,
+            StrideB,
+            stridesDs,
+            StrideC});
+
+        invoke_multi_d_gemm<
+                            ADataType,
+                                BDataType,
+                                DsDataType,
+                                AccDataType,
+                                CDataType,
+                                ALayout,
+                                BLayout,
+                                DsLayout,
+                                CLayout,
+                                CDEElementWiseFn>(args,
+                                                       ck_tile::stream_config{nullptr, false});
+
+        std::cout << "Run kernel with M =" << M << " N =" << N << " K =" << K
+                  << " StrideA =" << StrideA << " StrideB =" << StrideB << " StrideC =" << StrideC
+                  << " StrideD0 =" << StrideD0 << " StrideD1 =" << StrideD1
+                  << std::endl;
+
+        c_m_n_dev_buf.FromDevice(c_m_n_device_result.data());
+        bool pass = true;
+
+        ck_tile::HostTensor<CDataType> c_m_n_host_ref(
+            f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+        c_m_n_host_ref.SetZero();
+
+        ck_tile::reference_gemm_multiple_d<ADataType,
+        BDataType,
+        std::tuple<ck_tile::HostTensor<D0DataType>, ck_tile::HostTensor<D1DataType>>,
+        AccDataType,
+        CDataType,
+        CDEElementWiseFn>(
+            a_m_k_tesnor, b_k_n_tensors, std::tie(d0_m_n_tensors, d1_m_n_tensors), 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 */ 1, max_accumulated_value);
+        pass = ck_tile::check_err(c_m_n_device_result,
+                                  c_m_n_host_ref,
+                                  "Error: Incorrect results!",
+                                  rtol_atol.at(ck_tile::number<0>{}),
+                                  rtol_atol.at(ck_tile::number<1>{}));
+        std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
+                  << " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
+                  << std::endl;
+
+        EXPECT_TRUE(pass);
+    }
+};