diff --git a/cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp b/cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp
index 9a86c5d5c08..d17ed835c53 100644
--- a/cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp
+++ b/cpp/tensorrt_llm/batch_manager/cacheTransBuffer.cpp
@@ -18,12 +18,176 @@
 #include "cacheTransBuffer.h"
 #include "tensorrt_llm/common/envUtils.h"
 #include "tensorrt_llm/common/logger.h"
+#include "tensorrt_llm/common/opUtils.h"
+#include "tensorrt_llm/executor/executor.h"
 #include <NvInferRuntimeBase.h>
 #include <mutex>
 
 namespace tensorrt_llm::batch_manager::kv_cache_manager
 {
 
+class FabricMemory::Impl
+{
+public:
+    Impl(size_t size)
+        : mSize(size)
+    {
+        TLLM_CUDA_CHECK(cudaGetDevice(&mDeviceIdx));
+        CUmemAllocationHandleType const handle_type = CU_MEM_HANDLE_TYPE_FABRIC;
+        CUmemAllocationProp prop = {};
+        prop.requestedHandleTypes = handle_type;
+        prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
+        prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+        prop.location.id = mDeviceIdx;
+        prop.allocFlags.gpuDirectRDMACapable = 1;
+
+        size_t granularity{0};
+        TLLM_CU_CHECK(cuMemGetAllocationGranularity(&granularity, &prop, CU_MEM_ALLOC_GRANULARITY_MINIMUM));
+        mGranularity = granularity;
+        mAllocSize = (size + granularity - 1) / granularity * granularity;
+        TLLM_CU_CHECK(cuMemCreate(&mHandle, mAllocSize, &prop, 0));
+        TLLM_CU_CHECK(cuMemAddressReserve(&mDevicePtr, mAllocSize, mGranularity, 0, 0));
+        mPtr = reinterpret_cast<void*>(mDevicePtr);
+        CUmemAccessDesc accessDesc = {};
+        accessDesc.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+        accessDesc.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
+        accessDesc.location.id = mDeviceIdx;
+        TLLM_CU_CHECK(cuMemMap(mDevicePtr, mAllocSize, 0, mHandle, 0));
+        TLLM_CU_CHECK(cuMemSetAccess(mDevicePtr, mAllocSize, &accessDesc, 1));
+        TLLM_LOG_DEBUG("FabricMemory::Impl::Impl mAllocSize:%ld", mAllocSize);
+    }
+
+    ~Impl()
+    {
+        TLLM_LOG_DEBUG("FabricMemory::Impl::~Impl mAllocSize:%ld", mAllocSize);
+        TLLM_CU_CHECK(cuMemUnmap(mDevicePtr, mAllocSize));
+        TLLM_CU_CHECK(cuMemRelease(mHandle));
+        TLLM_CU_CHECK(cuMemAddressFree(mDevicePtr, mAllocSize));
+    }
+
+    [[nodiscard]] void* getPtr() const
+    {
+        return mPtr;
+    }
+
+    [[nodiscard]] size_t getSize() const
+    {
+        return mSize;
+    }
+
+private:
+    size_t mSize;
+    size_t mAllocSize;
+    size_t mGranularity;
+    void* mPtr;
+    CUdeviceptr mDevicePtr;
+    CUmemGenericAllocationHandle mHandle;
+    int mDeviceIdx;
+};
+
+FabricMemory::FabricMemory(size_t size)
+    : pImpl(std::make_unique<Impl>(size))
+{
+}
+
+FabricMemory::~FabricMemory() = default;
+
+FabricMemory::FabricMemory(FabricMemory&&) noexcept = default;
+FabricMemory& FabricMemory::operator=(FabricMemory&&) noexcept = default;
+
+void* FabricMemory::getPtr() const
+{
+    return pImpl->getPtr();
+}
+
+size_t FabricMemory::getSize() const
+{
+    return pImpl->getSize();
+}
+
+size_t FabricMemory::getAlignedSize(size_t size)
+{
+
+    auto alingedSizeFun = []()
+    {
+        int deviceIdx = -1;
+        TLLM_CUDA_CHECK(cudaGetDevice(&deviceIdx));
+        CUmemAllocationHandleType const handle_type = CU_MEM_HANDLE_TYPE_FABRIC;
+        CUmemAllocationProp prop = {};
+        prop.requestedHandleTypes = handle_type;
+        prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
+        prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+        prop.location.id = deviceIdx;
+        prop.allocFlags.gpuDirectRDMACapable = 1;
+
+        size_t granularity{0};
+        TLLM_CU_CHECK(cuMemGetAllocationGranularity(&granularity, &prop, CU_MEM_ALLOC_GRANULARITY_MINIMUM));
+        return granularity;
+    };
+    static size_t granularity = alingedSizeFun();
+
+    return (size + granularity - 1) / granularity * granularity;
+}
+
+bool FabricMemory::supportFbaricMemory()
+{
+#ifdef __aarch64__
+    auto support_fun = []()
+    {
+        int fabric_handle_supported{0};
+        int gpu_direct_rdma_with_cuda_vmm_supported{0};
+        int deviceIdx = 0;
+        TLLM_CUDA_CHECK(cudaGetDevice(&deviceIdx));
+        CUresult ret0 = cuDeviceGetAttribute(
+            &fabric_handle_supported, CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_FABRIC_SUPPORTED, deviceIdx);
+
+        CUresult ret1 = cuDeviceGetAttribute(&gpu_direct_rdma_with_cuda_vmm_supported,
+            CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WITH_CUDA_VMM_SUPPORTED, deviceIdx);
+        TLLM_LOG_DEBUG("FabricMemory::supportFbaricMemory fabric_handle_supported:%d", fabric_handle_supported);
+        TLLM_LOG_DEBUG("FabricMemory::supportFbaricMemory gpu_direct_rdma_with_cuda_vmm_supported:%d",
+            gpu_direct_rdma_with_cuda_vmm_supported);
+        if (ret0 != CUresult::CUDA_SUCCESS || ret1 != CUresult::CUDA_SUCCESS || fabric_handle_supported == 0
+            || gpu_direct_rdma_with_cuda_vmm_supported == 0)
+        {
+            return false;
+        }
+
+        CUmemAllocationHandleType const handle_type = CU_MEM_HANDLE_TYPE_FABRIC;
+        CUmemAllocationProp prop = {};
+        prop.requestedHandleTypes = handle_type;
+        prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
+        prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+        prop.location.id = deviceIdx;
+        prop.allocFlags.gpuDirectRDMACapable = 1;
+
+        size_t granularity{0};
+        TLLM_CU_CHECK(cuMemGetAllocationGranularity(&granularity, &prop, CU_MEM_ALLOC_GRANULARITY_MINIMUM));
+        CUmemGenericAllocationHandle handle;
+
+        auto cuRet = cuMemCreate(&handle, granularity, &prop, 0);
+
+        if (cuRet == CUresult::CUDA_SUCCESS)
+        {
+            TLLM_CU_CHECK(cuMemRelease(handle));
+            return true;
+        }
+        if (cuRet == CUresult::CUDA_ERROR_NOT_PERMITTED)
+        {
+            TLLM_LOG_WARNING("Try to creat fabric memory failed , setting imex channel may be required");
+            return false;
+        }
+        TLLM_CU_CHECK(cuRet);
+
+        return false;
+    };
+    static bool support = support_fun();
+    return support;
+
+#else
+    return false;
+#endif
+}
+
 CacheTransBufferManager::CacheTransBufferManager(
     KVCacheManager::BaseKVCacheManager* cacheManager, std::optional<size_t> maxNumTokens)
     : mCacheManager{cacheManager}
@@ -47,12 +211,19 @@ CacheTransBufferManager::CacheTransBufferManager(
     mOnlyUseDynamicBuffer = mTransferBufferSize == 0;
     mRecvBufferCount = common::getEnvRequestKVCacheConcurrent() ? common::getEnvKVCacheRecvBufferCount() : 1;
     mSendBufferCount = common::getEnvParallelCacheSend() ? common::getEnvKVCacheSendMaxConcurrenceNum() : 1;
+    mUseFabricMemory = !(common::getEnvKVCacheTransferUseSyncBuffer() || common::getEnvKVCacheTransferUseAsyncBuffer())
+        && FabricMemory::supportFbaricMemory();
+    if (mUseFabricMemory)
+    {
+        mTransferBufferSize = FabricMemory::getAlignedSize(mTransferBufferSize);
+    }
     mPreAllocBufferSize = mTransferBufferSize * (mRecvBufferCount + mSendBufferCount);
     TLLM_LOG_INFO(
         "CacheTransBufferManager: mMaxNumTokens:%ld, mRecvBufferCount:%ld, "
-        "mSendBufferCount:%ld,mTransferBufferSize:%ld, mPreAllocBufferSize:%ld,mOnlyUseDynamicBuffer:%d",
+        "mSendBufferCount:%ld,mTransferBufferSize:%ld, mPreAllocBufferSize:%ld,mOnlyUseDynamicBuffer:%d "
+        "mUseFabricMemory:%d",
         maxNumTokens.has_value() ? maxNumTokens.value() : 0, mRecvBufferCount, mSendBufferCount, mTransferBufferSize,
-        mPreAllocBufferSize, mOnlyUseDynamicBuffer);
+        mPreAllocBufferSize, mOnlyUseDynamicBuffer, mUseFabricMemory);
     bool to_allocate = common::getEnvUseMPIKvCache() || common::getEnvUseUCXKvCache() || common::getEnvUseNixlKvCache();
 
     TLLM_CHECK_WITH_INFO(to_allocate, "CacheTransBufferManager: to_allocate is false");
@@ -76,6 +247,12 @@ size_t CacheTransBufferManager::preAllocBufferSize(
     {
         TransferBufferSize = maxNumTokens.value() * kvCacheSizePerToken.value();
     }
+    bool useFabricMemory = FabricMemory::supportFbaricMemory()
+        && (!(common::getEnvKVCacheTransferUseSyncBuffer() || common::getEnvKVCacheTransferUseAsyncBuffer()));
+    if (useFabricMemory)
+    {
+        TransferBufferSize = FabricMemory::getAlignedSize(TransferBufferSize);
+    }
     size_t RecvBufferCount = common::getEnvRequestKVCacheConcurrent() ? common::getEnvKVCacheRecvBufferCount() : 1;
     size_t SendBufferCount = common::getEnvParallelCacheSend() ? common::getEnvKVCacheSendMaxConcurrenceNum() : 1;
     size_t PreAllocBufferSize = TransferBufferSize * (RecvBufferCount + SendBufferCount);
@@ -122,7 +299,6 @@ runtime::ITensor::SharedPtr CacheTransBufferManager::getSendBuffer(std::optional
     if (bufferId.has_value())
     {
         TLLM_CHECK(static_cast<size_t>(bufferId.value()) < mSendBufferCount);
-        // TLLM_CHECK(mConcurrenceSendResource.mBufferIndexFlag[bufferId.value()] == 1);
         return mConcurrenceSendResource.mBuffers[bufferId.value()];
     }
     return nullptr;
@@ -193,7 +369,23 @@ void CacheTransBufferManager::allocateBuffer()
     mBufferEleSize = mTransferBufferSize / common::getDTypeSize(mDataType);
     mConcurrenceSendResource.mBufferIndexFlag.resize(mSendBufferCount, 0);
     mConcurrenceRecvResource.mBufferIndexFlag.resize(mRecvBufferCount, 0);
-    if (common::getEnvKVCacheTransferUseAsyncBuffer())
+    if (mUseFabricMemory)
+    {
+        mFabricMemory.reserve(mSendBufferCount + mRecvBufferCount);
+        for (size_t i = 0; i < mSendBufferCount; i++)
+        {
+            mFabricMemory.emplace_back(std::make_unique<FabricMemory>(mTransferBufferSize));
+            mConcurrenceSendResource.mBuffers[i] = runtime::ITensor::wrap(mFabricMemory.back()->getPtr(), mDataType,
+                runtime::ITensor::makeShape({static_cast<int64_t>(mBufferEleSize)}), mBufferEleSize);
+        }
+        for (size_t i = 0; i < mRecvBufferCount; i++)
+        {
+            mFabricMemory.emplace_back(std::make_unique<FabricMemory>(mTransferBufferSize));
+            mConcurrenceRecvResource.mBuffers[i] = runtime::ITensor::wrap(mFabricMemory.back()->getPtr(), mDataType,
+                runtime::ITensor::makeShape({static_cast<int64_t>(mBufferEleSize)}), mBufferEleSize);
+        }
+    }
+    else if (common::getEnvKVCacheTransferUseAsyncBuffer())
     {
         for (size_t i = 0; i < mSendBufferCount; i++)
         {
diff --git a/cpp/tensorrt_llm/batch_manager/cacheTransBuffer.h b/cpp/tensorrt_llm/batch_manager/cacheTransBuffer.h
index 7a2248ddd6b..e18bc810803 100644
--- a/cpp/tensorrt_llm/batch_manager/cacheTransBuffer.h
+++ b/cpp/tensorrt_llm/batch_manager/cacheTransBuffer.h
@@ -30,6 +30,29 @@
 namespace tensorrt_llm::batch_manager::kv_cache_manager
 {
 
+class FabricMemory
+{
+public:
+    explicit FabricMemory(size_t size);
+    ~FabricMemory();
+
+    FabricMemory(FabricMemory const&) = delete;
+    FabricMemory& operator=(FabricMemory const&) = delete;
+
+    FabricMemory(FabricMemory&&) noexcept;
+    FabricMemory& operator=(FabricMemory&&) noexcept;
+
+    void* getPtr() const;
+    size_t getSize() const;
+
+    static size_t getAlignedSize(size_t size);
+    static bool supportFbaricMemory();
+
+private:
+    class Impl;
+    std::unique_ptr<Impl> pImpl;
+};
+
 class CacheTransBufferManager
 {
 public:
@@ -81,12 +104,14 @@ class CacheTransBufferManager
     size_t mSendBufferCount;
     size_t mTransferBufferSize;
     bool mOnlyUseDynamicBuffer;
+    bool mUseFabricMemory;
     size_t mBufferEleSize;
     nvinfer1::DataType mDataType;
     ConcurrenceResource mConcurrenceSendResource;
     ConcurrenceResource mConcurrenceRecvResource;
     KVCacheManager::BaseKVCacheManager* mCacheManager;
     runtime::BufferManager mBufferManager;
+    std::vector<std::unique_ptr<FabricMemory>> mFabricMemory;
 };
 
 } // namespace tensorrt_llm::batch_manager::kv_cache_manager
diff --git a/cpp/tensorrt_llm/batch_manager/dataTransceiver.cpp b/cpp/tensorrt_llm/batch_manager/dataTransceiver.cpp
index f0c12c3f232..88f1380e0b3 100644
--- a/cpp/tensorrt_llm/batch_manager/dataTransceiver.cpp
+++ b/cpp/tensorrt_llm/batch_manager/dataTransceiver.cpp
@@ -101,6 +101,7 @@ class DataResponder::Impl
     {
         TLLM_CHECK(mSender);
         TLLM_CUDA_CHECK(cudaGetDevice(&mDeviceId));
+        mCurrentRequest = std::nullopt;
         mResponseFuture = std::async(std::launch::async, &Impl::response, this);
     }
 
@@ -220,13 +221,10 @@ class DataResponder::Impl
                 }
                 else
                 {
-                    if (mCurrentRequest.has_value())
-                    {
-                        TLLM_LOG_ERROR(
-                            "This executor does not have a prepared KV cache for request ID: %zu, and the "
-                            "mReadyResponses size is: %zu.",
-                            mCurrentRequest.value(), mReadyResponses.size());
-                    }
+                    TLLM_CHECK_WITH_INFO(!mCurrentRequest.has_value(),
+                        "This executor does not have a prepared KV cache for request ID: %zu, and the "
+                        "mReadyResponses size is: %zu. mpi rank :%d     ",
+                        mCurrentRequest.value(), mReadyResponses.size(), mpi::MpiComm::world().getRank());
                     std::unique_lock lk(mCondMutex);
                     mResponderCv.wait(lk, [this]() { return (mAnyReady || mTerminate); });
                 }
diff --git a/cpp/tensorrt_llm/common/envUtils.cpp b/cpp/tensorrt_llm/common/envUtils.cpp
index 665ba032ad2..0981a40efc8 100644
--- a/cpp/tensorrt_llm/common/envUtils.cpp
+++ b/cpp/tensorrt_llm/common/envUtils.cpp
@@ -365,6 +365,12 @@ bool getEnvKVCacheTransferUseAsyncBuffer()
     return useAsyncBuffer;
 }
 
+bool getEnvKVCacheTransferUseSyncBuffer()
+{
+    static bool const useSyncBuffer = getBoolEnv("TRTLLM_KVCACHE_TRANSFER_USE_SYNC_BUFFER");
+    return useSyncBuffer;
+}
+
 size_t getEnvKVCacheSendMaxConcurrenceNum()
 {
 
diff --git a/cpp/tensorrt_llm/common/envUtils.h b/cpp/tensorrt_llm/common/envUtils.h
index 5bd250c6f6a..54801f8344e 100644
--- a/cpp/tensorrt_llm/common/envUtils.h
+++ b/cpp/tensorrt_llm/common/envUtils.h
@@ -92,6 +92,8 @@ size_t getEnvKVCacheRecvBufferCount();
 
 bool getEnvKVCacheTransferUseAsyncBuffer();
 
+bool getEnvKVCacheTransferUseSyncBuffer();
+
 size_t getEnvKVCacheSendMaxConcurrenceNum();
 
 size_t getEnvMemSizeForKVCacheTransferBuffer();
diff --git a/cpp/tests/batch_manager/cacheTransceiverTest.cpp b/cpp/tests/batch_manager/cacheTransceiverTest.cpp
index 8981960da11..d6f14525ded 100644
--- a/cpp/tests/batch_manager/cacheTransceiverTest.cpp
+++ b/cpp/tests/batch_manager/cacheTransceiverTest.cpp
@@ -1203,7 +1203,8 @@ TEST_P(AsymmetricalCacheTestWithDP, TestCase)
         int requestId = 0;
         for (auto len : {30, 10, 60, 30, 60, 10})
         {
-            requests.emplace_back(makeLlmRequestWithDP(len, requestId++, requestId % contextTp));
+            requests.emplace_back(makeLlmRequestWithDP(len, requestId, requestId % contextTp));
+            requestId++;
         }
         std::vector<std::future<void>> contextFutures;
         std::vector<std::future<void>> generationFutures;
@@ -1216,7 +1217,7 @@ TEST_P(AsymmetricalCacheTestWithDP, TestCase)
             {
                 for (int i = 0; i < requests.size(); i++)
                 {
-                    if (i % mTpSize == mTpRank)
+                    if ((i) % mTpSize == mTpRank)
                     {
                         // round robin
                         contextRequests.push_back(requests[i]);
@@ -1239,7 +1240,7 @@ TEST_P(AsymmetricalCacheTestWithDP, TestCase)
             {
                 for (int i = 0; i < requests.size(); i++)
                 {
-                    if (i % mTpSize == mTpRank)
+                    if ((i) % mTpSize == mTpRank)
                     {
                         generationRequests.push_back(requests[i]);
                     }