diff --git a/python/tutorials/09-persistent-matmul.py b/python/tutorials/09-persistent-matmul.py
index 3078e691b7..6a9a8baff9 100644
--- a/python/tutorials/09-persistent-matmul.py
+++ b/python/tutorials/09-persistent-matmul.py
@@ -650,7 +650,7 @@ def torch_matmul(a, b):
     N, K = b.shape
     bytes_per_elem = a.element_size()
     flops_str = f"flops{bytes_per_elem * 8}"
-    if is_cuda():
+    if os.name != "nt":
         with proton.scope(f"torch [M={M}, N={N}, K={K}]",
                           {"bytes": bytes_per_elem * (M * K + N * K + M * N), flops_str: 2. * M * N * K}):
             c = torch.matmul(a, b.T)
@@ -672,8 +672,7 @@ def bench_fn(label, reps, warmup_reps, fn, *args):
     print(f"Benchmarking {label}: ...", end="")
     for _ in range(warmup_reps):
         fn(*args)
-    #FIXME: Enable for XPU once proton support works.
-    if is_cuda():
+    if os.name != "nt":
         with proton_context():
             for _ in range(reps):
                 fn(*args)
@@ -783,11 +782,11 @@ def show_profile(precision, profile_name):
 
         validate(32, 32, 32, dtype)
         validate(8192, 8192, args.K_range[0], dtype)
-        if is_cuda():
+        if os.name != "nt":
             proton.start("matmul", hook="triton")
             proton.deactivate()
         for K in range(args.K_range[0], args.K_range[1] + 1, args.K_step):
             bench(K, dtype)
-        if is_cuda():
+        if os.name != "nt":
             proton.finalize()
             show_profile(args.prec, "matmul")