diff --git a/example/test_linear.py b/example/test_linear.py
index c2ad4db..8ccbbbc 100644
--- a/example/test_linear.py
+++ b/example/test_linear.py
@@ -118,7 +118,11 @@ def load_specified_linear_weights():
 
 model.post_init()
 
-res = model(x)
+stream = torch.cuda.Stream()
+with torch.cuda.stream(stream):
+    res = model(x)
+stream.synchronize()
+
 print(f'tm.linear.res: {res}')
 abs_diff = torch.abs(res - ref_res).float()
 rel_diff = abs_diff / torch.max(torch.abs(ref_res), torch.abs(res))
diff --git a/src/turbomind/api/python/bind.cpp b/src/turbomind/api/python/bind.cpp
index 8140b46..5eadc1e 100644
--- a/src/turbomind/api/python/bind.cpp
+++ b/src/turbomind/api/python/bind.cpp
@@ -329,16 +329,17 @@ PYBIND11_MODULE(_turbomind_ext, m) {
         .def(py::init([](size_t in_features, size_t out_features, int w_bit, int group_size) {
             return new turbomind::Linear(in_features, out_features, w_bit, group_size);
         }))
-        .def("post_init", [](turbomind::Linear* linear, py::object qweight, py::object scales, py::object qzeros,
+        .def("post_init", [](turbomind::Linear* self, py::object qweight, py::object scales, py::object qzeros,
                              bool simt){
             auto _qweight = TorchTensorToTurbomindTensor(qweight);
             auto _scales = TorchTensorToTurbomindTensor(scales);
             auto _qzeros = TorchTensorToTurbomindTensor(qzeros);
-            linear->post_init(_qweight, *_scales, *_qzeros, simt);
+            self->post_init(_qweight, *_scales, *_qzeros, simt);
         })
-        .def("forward", [](turbomind::Linear* linear, py::object in, py::object out) {
+        .def("forward", [](turbomind::Linear* self, py::object in, py::object out, int64_t stream_id = 0) {
             auto _in = TorchTensorToTurbomindTensor(in);
             auto _out = TorchTensorToTurbomindTensor(out);
-            return linear->forward(*_in, *_out);
+            auto stream = reinterpret_cast<cudaStream_t>(stream_id);
+            return self->forward(*_in, *_out, stream);
         });
 }
diff --git a/src/turbomind/api/python/linear.cc b/src/turbomind/api/python/linear.cc
index 6641110..d05c7d4 100644
--- a/src/turbomind/api/python/linear.cc
+++ b/src/turbomind/api/python/linear.cc
@@ -36,15 +36,15 @@ struct Linear::Impl {
 
         workspace_.barriers_size = gemm::Gemm::kBarriersSize;
         workspace_.partials_size = gemm::Gemm::kPartialsSize;
-        cudaMallocAsync(&workspace_.barriers, workspace_.barriers_size, stream_);
-        cudaMallocAsync(&workspace_.partials, workspace_.partials_size, stream_);
-        cudaMemsetAsync(workspace_.barriers, 0, workspace_.barriers_size, stream_);
+        cudaMallocAsync(&workspace_.barriers, workspace_.barriers_size, 0);
+        cudaMallocAsync(&workspace_.partials, workspace_.partials_size, 0);
+        cudaMemsetAsync(workspace_.barriers, 0, workspace_.barriers_size, 0);
     }
 
     ~Impl()
     {
-        cudaFreeAsync(workspace_.barriers, stream_);
-        cudaFreeAsync(workspace_.partials, stream_);
+        cudaFreeAsync(workspace_.barriers, 0);
+        cudaFreeAsync(workspace_.partials, 0);
         workspace_ = {};
         check_cuda_error(cudaFree(scales_zeros_));
     }
@@ -60,7 +60,7 @@ struct Linear::Impl {
         check_cuda_error(cudaFree(workspace));
     }
 
-    void forward(const Tensor& in, Tensor& out) {
+    void forward(const Tensor& in, Tensor& out, cudaStream_t stream) {
         TM_CHECK(in.type == TYPE_FP16 && out.type == TYPE_FP16);
         TM_CHECK(in.shape.size() == 2 && in.shape[1] == input_dims_);
         TM_CHECK(out.shape.size() == 2 && out.shape[0] == in.shape[0] && out.shape[1] == output_dims_);
@@ -106,7 +106,7 @@ struct Linear::Impl {
                             const_cast<void*>(out.data),
                             c_desc,
                             workspace_,
-                            stream_);
+                            stream);
 
         if (ec) {
             printf("%s: %d", __PRETTY_FUNCTION__, ec);
@@ -266,7 +266,6 @@ struct Linear::Impl {
     gemm::Gemm           gemm_;
     gemm::DispatchPolicy dispatch_policy_{gemm::DispatchPolicy::kDefault};
     gemm::Workspace      workspace_;
-    cudaStream_t         stream_{};
 
     size_t input_dims_;
     size_t output_dims_;
@@ -288,8 +287,8 @@ void Linear::post_init(std::shared_ptr<Tensor> qweight, const Tensor& scales, co
     impl_->post_init(qweight, scales, qzeros, simt);
 }
 
-void Linear::forward(const Tensor& in, Tensor& out)
+void Linear::forward(const Tensor& in, Tensor& out, cudaStream_t stream)
 {
-    impl_->forward(in, out);
+    impl_->forward(in, out, stream);
 }
 }  // namespace turbomind
diff --git a/src/turbomind/api/python/linear.h b/src/turbomind/api/python/linear.h
index 11e81eb..8b611f2 100644
--- a/src/turbomind/api/python/linear.h
+++ b/src/turbomind/api/python/linear.h
@@ -28,7 +28,7 @@ class Linear {
     Linear(size_t input_dims, size_t output_dims, int w_bit, int group_size);
     void post_init(std::shared_ptr<Tensor> qweight, const Tensor& scales, const Tensor& qzeros,
                    bool simt);
-    void forward(const Tensor& in, Tensor& out);
+    void forward(const Tensor& in, Tensor& out, cudaStream_t stream = nullptr);
     ~Linear() {}
 
 private:
diff --git a/turbomind/linear.py b/turbomind/linear.py
index 1e9d221..a7fea9b 100644
--- a/turbomind/linear.py
+++ b/turbomind/linear.py
@@ -163,7 +163,8 @@ def forward(self, x):
             dtype=torch.float16,
             device=x.device,
         )
-        self.linear.forward(x, out)
+        stream = torch.cuda.current_stream()
+        self.linear.forward(x, out, stream.cuda_stream)
         out = torch.from_dlpack(out)
         if self.bias is not None:
             out.add_(self.bias)