diff --git a/apps/nccl/src/allreduce.cu b/apps/nccl/src/allreduce.cu
index 0a48db61b..bac933a01 100644
--- a/apps/nccl/src/allreduce.cu
+++ b/apps/nccl/src/allreduce.cu
@@ -123,6 +123,26 @@ struct Allreduce8Adapter {
   }
 };
 
+template <Op OpType, typename T>
+struct AllreduceNvlsPacketAdapter {
+  static cudaError_t call(const void* input, void* scratch, void* output, void*, void*,
+                          mscclpp::DeviceHandle<mscclpp::SwitchChannel>* nvlsChannels,
+                          mscclpp::DeviceHandle<mscclpp::SwitchChannel>*, size_t, size_t, size_t scratchBufferSize,
+                          int rank, int, int worldSize, size_t nelems, cudaStream_t stream, uint32_t* deviceFlag,
+                          uint32_t*, uint32_t*, uint32_t) {
+    size_t size = nelems * sizeof(T);
+    int nBlocks = 8;
+    int nThreadsPerBlock = 1024;
+    if (size <= (1 << 13)) {
+      nBlocks = 4;
+      nThreadsPerBlock = 512;
+    }
+    allreduceNvlsPacket<OpType, T><<<nBlocks, nThreadsPerBlock, 0, stream>>>(
+        (const T*)input, (T*)scratch, (T*)output, nvlsChannels, nelems, scratchBufferSize, rank, worldSize, deviceFlag);
+    return cudaGetLastError();
+  }
+};
+
 template <template <Op, typename> class Adapter>
 AllreduceFunc dispatch(ncclRedOp_t op, ncclDataType_t dtype) {
   Op reduceOp = getReduceOp(op);
@@ -558,4 +578,76 @@ mscclpp::Algorithm Allreduce8::build() {
         return self->generateAllreduceContextKey(input, output, count, static_cast<ncclDataType_t>(dtype));
       });
   return allreduceAlgo;
+}
+
+void AllreduceNvlsPacket::initialize(std::shared_ptr<mscclpp::Communicator>,
+                                     std::unordered_map<std::string, std::shared_ptr<void>>& extras) {
+  this->scratchBuffer_ = std::static_pointer_cast<char>(extras.at("scratch"));
+  this->scratchBufferSize_ = *(size_t*)(extras.at("scratch_size").get());
+  deviceFlag_ = mscclpp::detail::gpuCallocShared<uint32_t>(16);
+  std::vector<uint32_t> initFlag(16);
+  for (int i = 0; i < 16; ++i) {
+    initFlag[i] = 1;
+  }
+  mscclpp::gpuMemcpy<uint32_t>(deviceFlag_.get(), initFlag.data(), 16, cudaMemcpyHostToDevice);
+}
+
+mscclpp::AlgorithmCtxKey AllreduceNvlsPacket::generateAllreduceContextKey(const void*, void*, size_t, ncclDataType_t) {
+  return mscclpp::AlgorithmCtxKey{nullptr, nullptr, 0, 0, 0};
+}
+
+std::shared_ptr<mscclpp::AlgorithmCtx> AllreduceNvlsPacket::initAllreduceContext(
+    std::shared_ptr<mscclpp::Communicator> comm, const void*, void*, size_t, ncclDataType_t) {
+  auto ctx = std::make_shared<mscclpp::AlgorithmCtx>();
+  ctx->rank = comm->bootstrap()->getRank();
+  ctx->workSize = comm->bootstrap()->getNranks();
+  ctx->nRanksPerNode = comm->bootstrap()->getNranksPerNode();
+
+  // setup channels
+  int nSwitchChannels = 1;
+  ctx->nvlsConnections = setupNvlsConnections(comm, nvlsBufferSize_, nSwitchChannels);
+  ctx->switchChannels =
+      setupNvlsChannels(ctx->nvlsConnections, this->scratchBuffer_.get(), this->scratchBufferSize_, nSwitchChannels);
+  ctx->switchChannelDeviceHandles = setupNvlsChannelDeviceHandles(ctx->switchChannels);
+  return ctx;
+}
+
+ncclResult_t AllreduceNvlsPacket::allreduceKernelFunc(const std::shared_ptr<mscclpp::AlgorithmCtx> ctx,
+                                                      const void* input, void* output, size_t count,
+                                                      ncclDataType_t dtype, cudaStream_t stream,
+                                                      std::unordered_map<std::string, std::shared_ptr<void>>& extra) {
+  int op = *static_cast<int*>(extra.at("op").get());
+  AllreduceFunc allreduce = dispatch<AllreduceNvlsPacketAdapter>(static_cast<ncclRedOp_t>(op), dtype);
+  if (!allreduce) {
+    WARN("Unsupported operation or data type for allreduce, dtype=%d", dtype);
+    return ncclInvalidArgument;
+  }
+  cudaError_t error =
+      allreduce(input, this->scratchBuffer_.get(), output, nullptr, nullptr, ctx->switchChannelDeviceHandles.get(),
+                nullptr, 0, 0, this->scratchBufferSize_, ctx->rank, ctx->nRanksPerNode, ctx->workSize, count, stream,
+                this->deviceFlag_.get(), nullptr, nullptr, 0);
+  if (error != cudaSuccess) {
+    WARN("AllreduceNvlsPacket failed with error: %s", cudaGetErrorString(error));
+    return ncclUnhandledCudaError;
+  }
+  return ncclSuccess;
+}
+
+mscclpp::Algorithm AllreduceNvlsPacket::build() {
+  auto self = std::make_shared<AllreduceNvlsPacket>();
+  mscclpp::Algorithm allreduceAlgo(
+      "default_allreduce_nvls_packet", "allreduce",
+      [self](std::shared_ptr<mscclpp::Communicator> comm,
+             std::unordered_map<std::string, std::shared_ptr<void>>& extras) { self->initialize(comm, extras); },
+      [self](const std::shared_ptr<mscclpp::AlgorithmCtx> ctx, const void* input, void* output, size_t count, int dtype,
+             cudaStream_t stream, std::unordered_map<std::string, std::shared_ptr<void>>& extras) {
+        return self->allreduceKernelFunc(ctx, input, output, count, static_cast<ncclDataType_t>(dtype), stream, extras);
+      },
+      [self](std::shared_ptr<mscclpp::Communicator> comm, const void* input, void* output, size_t count, int dtype) {
+        return self->initAllreduceContext(comm, input, output, count, static_cast<ncclDataType_t>(dtype));
+      },
+      [self](const void* input, void* output, size_t count, int dtype) {
+        return self->generateAllreduceContextKey(input, output, count, static_cast<ncclDataType_t>(dtype));
+      });
+  return allreduceAlgo;
 }
\ No newline at end of file
diff --git a/apps/nccl/src/allreduce.hpp b/apps/nccl/src/allreduce.hpp
index e2315146f..7f9c5f245 100644
--- a/apps/nccl/src/allreduce.hpp
+++ b/apps/nccl/src/allreduce.hpp
@@ -793,6 +793,44 @@ __global__ void __launch_bounds__(1024, 1)
 #endif
 }
 
+template <Op OpType, typename T>
+__global__ void __launch_bounds__(1024, 1)
+    allreduceNvlsPacket([[maybe_unused]] const T* input, [[maybe_unused]] T* scratch, [[maybe_unused]] T* output,
+                        [[maybe_unused]] mscclpp::DeviceHandle<mscclpp::SwitchChannel>* multicast,
+                        [[maybe_unused]] size_t nelems, [[maybe_unused]] size_t scratchBufferSize,
+                        [[maybe_unused]] int rank, [[maybe_unused]] int worldSize,
+                        [[maybe_unused]] uint32_t* deviceFlag) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 900
+  uint32_t flag = deviceFlag[blockIdx.x];
+  size_t scratchBaseOffset = (flag % 2) ? scratchBufferSize / 2 : 0;
+  uint32_t tid = threadIdx.x + blockIdx.x * blockDim.x;
+  uint32_t nPktPerRank = nelems / worldSize / (sizeof(mscclpp::LL8Packet::Payload) / sizeof(T));
+  mscclpp::LL8Packet* multiPkt =
+      (mscclpp::LL8Packet*)((char*)multicast->mcPtr + scratchBaseOffset) + rank * worldSize * nPktPerRank;
+  uint* src = (uint*)(input);
+  uint* dst = (uint*)(output);
+  mscclpp::LL8Packet* scratchPkt = (mscclpp::LL8Packet*)((char*)scratch + scratchBaseOffset);
+  for (uint32_t i = tid; i < nPktPerRank * worldSize; i += blockDim.x * gridDim.x) {
+    mscclpp::LL8Packet pkt(src[i], flag);
+    mscclpp::SwitchChannelDeviceHandle::multimemStore(*(mscclpp::f32x2*)(&pkt), multiPkt + i);
+  }
+  for (uint32_t i = tid; i < nPktPerRank * worldSize; i += blockDim.x * gridDim.x) {
+    uint data = src[i];
+    for (int peer = 0; peer < worldSize; peer++) {
+      if (peer == rank) {
+        continue;
+      }
+      uint val = scratchPkt[peer * worldSize * nPktPerRank + i].read(flag);
+      data = cal_vectors<T, OpType>(data, val);
+    }
+    dst[i] = data;
+  }
+  if (threadIdx.x == 0) {
+    deviceFlag[blockIdx.x] = deviceFlag[blockIdx.x] + 1;
+  }
+#endif
+}
+
 enum Op getReduceOp(ncclRedOp_t op);
 
 class AllreducePacket : public mscclpp::AlgorithmBuilder {
@@ -896,4 +934,28 @@ class Allreduce8 : public mscclpp::AlgorithmBuilder {
       memoryChannelsMap_;
 };
 
+class AllreduceNvlsPacket : public mscclpp::AlgorithmBuilder {
+ public:
+  mscclpp::Algorithm build() override;
+
+ private:
+  void initialize(std::shared_ptr<mscclpp::Communicator> comm,
+                  std::unordered_map<std::string, std::shared_ptr<void>>& extras);
+  ncclResult_t allreduceKernelFunc(const std::shared_ptr<mscclpp::AlgorithmCtx> ctx, const void* input, void* output,
+                                   size_t count, ncclDataType_t dtype, cudaStream_t stream,
+                                   std::unordered_map<std::string, std::shared_ptr<void>>& extras);
+
+  std::shared_ptr<mscclpp::AlgorithmCtx> initAllreduceContext(std::shared_ptr<mscclpp::Communicator> comm, const void*,
+                                                              void* output, size_t, ncclDataType_t);
+  mscclpp::AlgorithmCtxKey generateAllreduceContextKey(const void*, void*, size_t, ncclDataType_t);
+
+  size_t scratchBufferSize_;
+  std::shared_ptr<char> scratchBuffer_;
+  const int nSegmentsForScratchBuffer_ = 2;
+  const size_t nvlsBufferSize_ = (1 << 30);
+
+  std::shared_ptr<uint32_t> deviceFlag_;
+  std::shared_ptr<mscclpp::AlgorithmCtx> ctx_;
+};
+
 #endif  // ALLREDUCE_KERNEL_H
diff --git a/apps/nccl/src/nccl.cu b/apps/nccl/src/nccl.cu
index 3fc95f319..7e1c605ad 100644
--- a/apps/nccl/src/nccl.cu
+++ b/apps/nccl/src/nccl.cu
@@ -271,10 +271,12 @@ static void registerCustomizedAlgo() {
   std::shared_ptr<AllreduceNvls> allreduceNvlsAlgo = std::make_shared<AllreduceNvls>();
   std::shared_ptr<AllreduceNvlsWithCopy> allreduceNvlsWithCopyAlgo = std::make_shared<AllreduceNvlsWithCopy>();
   std::shared_ptr<Allreduce8> allreduceAllreduce8Algo = std::make_shared<Allreduce8>();
+  std::shared_ptr<AllreduceNvlsPacket> allreduceNvlsPacketAlgo = std::make_shared<AllreduceNvlsPacket>();
   collectionBuilder->addAlgorithmBuilder(allreduceAllpairAlgo);
   collectionBuilder->addAlgorithmBuilder(allreduceNvlsAlgo);
   collectionBuilder->addAlgorithmBuilder(allreduceNvlsWithCopyAlgo);
   collectionBuilder->addAlgorithmBuilder(allreduceAllreduce8Algo);
+  collectionBuilder->addAlgorithmBuilder(allreduceNvlsPacketAlgo);
 }
 
 static mscclpp::Algorithm algoSelector(
@@ -284,10 +286,11 @@ static mscclpp::Algorithm algoSelector(
     // Fallback to nccl/rccl when multi-node
     return mscclpp::Algorithm();
   }
+  static bool mscclppDisableChannelCache = mscclpp::env()->disableChannelCache;
+  static bool isNvlsSupported = mscclpp::isNvlsSupported();
   bool isCuMemMapAllocated =
       mscclpp::isCuMemMapAllocated(const_cast<void*>(input)) && mscclpp::isCuMemMapAllocated(output);
-  bool mscclppDisableChannelCache = mscclpp::env()->disableChannelCache;
-  bool useNvlsWithZeroCopy = mscclpp::isNvlsSupported() && !mscclppDisableChannelCache && isCuMemMapAllocated;
+  bool useNvlsWithZeroCopy = isNvlsSupported && !mscclppDisableChannelCache && isCuMemMapAllocated;
   if (collective == "allgather") {
     if (messageSize <= 32 * (1 << 20)) {
       return algoMapByCollective.at(collective).at("default_allgather6");
@@ -302,21 +305,25 @@ static mscclpp::Algorithm algoSelector(
     }
   }
   if (collective == "allreduce") {
+    if (messageSize <= (1 << 15) && isNvlsSupported) {
+      return algoMapByCollective.at(collective).at("default_allreduce_nvls_packet");
+    }
     if (messageSize <= (1 << 16) || (messageSize <= (1 << 20) && !useNvlsWithZeroCopy)) {
       return algoMapByCollective.at(collective).at("default_allreduce_packet");
-    } else if (useNvlsWithZeroCopy) {
+    }
+    if (useNvlsWithZeroCopy) {
       return algoMapByCollective.at(collective).at("default_allreduce_nvls");
-    } else if (mscclpp::isNvlsSupported()) {
+    }
+    if (mscclpp::isNvlsSupported()) {
       return algoMapByCollective.at(collective).at("default_allreduce_nvls_with_copy");
-    } else {
+    }
 #if defined(__HIP_PLATFORM_AMD__)
-      return algoMapByCollective.at(collective).at("default_allreduce_allreduce8");
+    return algoMapByCollective.at(collective).at("default_allreduce_allreduce8");
 #else
-      if (!mscclppNcclDlopenSharedLib) {
-        return algoMapByCollective.at(collective).at("default_allreduce_allreduce8");
-      }
-#endif
+    if (!mscclppNcclDlopenSharedLib) {
+      return algoMapByCollective.at(collective).at("default_allreduce_allreduce8");
     }
+#endif
   }
   INFO(MSCCLPP_NCCL, "Failed to get algo from customized kernel, fallback to nccl/rccl");
   return mscclpp::Algorithm();