diff --git a/python/examples/pytorch_bench.py b/python/examples/pytorch_bench.py
new file mode 100644
index 0000000..f62a7a5
--- /dev/null
+++ b/python/examples/pytorch_bench.py
@@ -0,0 +1,58 @@
+import sys
+
+import cuda.nvbench as nvbench
+import torch
+
+
+def as_torch_cuda_Stream(
+    cs: nvbench.CudaStream, dev: int | None
+) -> torch.cuda.ExternalStream:
+    return torch.cuda.ExternalStream(
+        stream_ptr=cs.addressof(), device=torch.cuda.device(dev)
+    )
+
+
+def torch_bench(state: nvbench.State) -> None:
+    state.set_throttle_threshold(0.25)
+
+    dev_id = state.get_device()
+    tc_s = as_torch_cuda_Stream(state.get_stream(), dev_id)
+
+    dt = torch.float32
+    scalar_shape: tuple = tuple()
+    n = 2**28
+    with torch.cuda.stream(tc_s):
+        a3 = torch.randn(scalar_shape, dtype=dt)
+        a2 = torch.randn(scalar_shape, dtype=dt)
+        a1 = torch.randn(scalar_shape, dtype=dt)
+        a0 = torch.randn(scalar_shape, dtype=dt)
+        x = torch.linspace(-3, 3, n, dtype=dt)
+        y = torch.sin(x)
+
+    learning_rate = 1e-4
+
+    def launcher(launch: nvbench.Launch) -> None:
+        tc_s = as_torch_cuda_Stream(launch.get_stream(), dev_id)
+        with torch.cuda.stream(tc_s):
+            x2 = torch.square(x)
+            y_pred = (a3 + x2 * a1) + x * (a2 + a0 * x2)
+
+            _ = torch.square(y_pred - y).sum()
+            grad_y_pred = 2 * (y_pred - y)
+            grad_a3 = grad_y_pred.sum()
+            grad_a2 = (grad_y_pred * x).sum()
+            grad_a1 = (grad_y_pred * x2).sum()
+            grad_a0 = (grad_y_pred * x2 * x).sum()
+
+            _ = a3 - grad_a3 * learning_rate
+            _ = a2 - grad_a2 * learning_rate
+            _ = a1 - grad_a1 * learning_rate
+            _ = a0 - grad_a0 * learning_rate
+
+    state.exec(launcher, sync=True)
+
+
+if __name__ == "__main__":
+    nvbench.register(torch_bench)
+
+    nvbench.run_all_benchmarks(sys.argv)