diff --git a/cuda_core/examples/pytorch_example.py b/cuda_core/examples/pytorch_example.py
new file mode 100644
index 000000000..76a972859
--- /dev/null
+++ b/cuda_core/examples/pytorch_example.py
@@ -0,0 +1,106 @@
+# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. ALL RIGHTS RESERVED.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+## Usage: pip install "cuda-core[cu12]"
+## python python_example.py
+import sys
+
+import torch
+
+from cuda.core.experimental import Device, LaunchConfig, Program, ProgramOptions, launch
+
+# SAXPY kernel - passing a as a pointer to avoid any type issues
+code = """
+template<typename T>
+__global__ void saxpy_kernel(const T* a, const T* x, const T* y, T* out, size_t N) {
+ const unsigned int tid = threadIdx.x + blockIdx.x * blockDim.x;
+ if (tid < N) {
+   // Dereference a to get the scalar value
+   out[tid] = (*a) * x[tid] + y[tid];
+ }
+}
+"""
+
+dev = Device()
+dev.set_current()
+
+# Get PyTorch's current stream
+pt_stream = torch.cuda.current_stream()
+print(f"PyTorch stream: {pt_stream}")
+
+
+# Create a wrapper class that implements __cuda_stream__
+class PyTorchStreamWrapper:
+    def __init__(self, pt_stream):
+        self.pt_stream = pt_stream
+
+    def __cuda_stream__(self):
+        stream_id = self.pt_stream.cuda_stream
+        return (0, stream_id)  # Return format required by CUDA Python
+
+
+s = PyTorchStreamWrapper(pt_stream)
+
+# prepare program
+arch = "".join(f"{i}" for i in dev.compute_capability)
+program_options = ProgramOptions(std="c++11", arch=f"sm_{arch}")
+prog = Program(code, code_type="c++", options=program_options)
+mod = prog.compile(
+    "cubin",
+    logs=sys.stdout,
+    name_expressions=("saxpy_kernel<float>", "saxpy_kernel<double>"),
+)
+
+# Run in single precision
+ker = mod.get_kernel("saxpy_kernel<float>")
+dtype = torch.float32
+
+# prepare input/output
+size = 64
+# Use a single element tensor for 'a'
+a = torch.tensor([10.0], dtype=dtype, device="cuda")
+x = torch.rand(size, dtype=dtype, device="cuda")
+y = torch.rand(size, dtype=dtype, device="cuda")
+out = torch.empty_like(x)
+
+# prepare launch
+block = 32
+grid = int((size + block - 1) // block)
+config = LaunchConfig(grid=grid, block=block)
+ker_args = (a.data_ptr(), x.data_ptr(), y.data_ptr(), out.data_ptr(), size)
+
+# launch kernel on our stream
+launch(s, config, ker, *ker_args)
+
+# check result
+assert torch.allclose(out, a.item() * x + y)
+print("Single precision test passed!")
+
+# let's repeat again with double precision
+ker = mod.get_kernel("saxpy_kernel<double>")
+dtype = torch.float64
+
+# prepare input
+size = 128
+# Use a single element tensor for 'a'
+a = torch.tensor([42.0], dtype=dtype, device="cuda")
+x = torch.rand(size, dtype=dtype, device="cuda")
+y = torch.rand(size, dtype=dtype, device="cuda")
+
+# prepare output
+out = torch.empty_like(x)
+
+# prepare launch
+block = 64
+grid = int((size + block - 1) // block)
+config = LaunchConfig(grid=grid, block=block)
+ker_args = (a.data_ptr(), x.data_ptr(), y.data_ptr(), out.data_ptr(), size)
+
+# launch kernel on PyTorch's stream
+launch(s, config, ker, *ker_args)
+
+# check result
+assert torch.allclose(out, a * x + y)
+print("Double precision test passed!")
+print("All tests passed successfully!")
diff --git a/cuda_core/tests/example_tests/utils.py b/cuda_core/tests/example_tests/utils.py
index 3b8e9b236..229851a2f 100644
--- a/cuda_core/tests/example_tests/utils.py
+++ b/cuda_core/tests/example_tests/utils.py
@@ -37,7 +37,7 @@ def run_example(samples_path, filename, env=None):
         exec(script, env if env else {})  # nosec B102
     except ImportError as e:
         # for samples requiring any of optional dependencies
-        for m in ("cupy",):
+        for m in ("cupy", "torch"):
             if f"No module named '{m}'" in str(e):
                 pytest.skip(f"{m} not installed, skipping related tests")
                 break