diff --git a/python/tutorials/03-matrix-multiplication.py b/python/tutorials/03-matrix-multiplication.py
index 7b838b17a279..327a2470752b 100644
--- a/python/tutorials/03-matrix-multiplication.py
+++ b/python/tutorials/03-matrix-multiplication.py
@@ -161,11 +161,6 @@ def is_cuda():
     return triton.runtime.driver.active.get_current_target().backend == "cuda"
 
 
-def is_hip_mi200():
-    target = triton.runtime.driver.active.get_current_target()
-    return target.backend == 'hip' and target.arch == 'gfx90a'
-
-
 def get_cuda_autotune_config():
     return [
         triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3,
@@ -231,6 +226,15 @@ def get_autotune_config():
         return get_hip_autotune_config()
 
 
+def estimate_matmul_error(a, b):
+    c = torch.matmul(a, b).to(torch.float64)
+    a_double = a.to(torch.float64)
+    b_double = b.to(torch.float64)
+    c_accurate = torch.matmul(a_double, b_double)
+    forward_error = torch.norm(c_accurate - c)
+    return forward_error
+
+
 # `triton.jit`'ed functions can be auto-tuned by using the `triton.autotune` decorator, which consumes:
 #   - A list of `triton.Config` objects that define different configurations of
 #       meta-parameters (e.g., `BLOCK_SIZE_M`) and compilation options (e.g., `num_warps`) to try
@@ -363,11 +367,11 @@ def matmul(a, b, activation=""):
 torch_output = torch.matmul(a, b)
 print(f"triton_output_with_fp16_inputs={triton_output}")
 print(f"torch_output_with_fp16_inputs={torch_output}")
-# Bigger tolerance for AMD MI200 devices.
-# MI200 devices use reduced precision fp16 and bf16 and flush input and
-# output denormal values to zero. Detailed info is at: https://pytorch.org/docs/stable/notes/numerical_accuracy.html#reduced-precision-fp16-and-bf16-gemms-and-convolutions-on-amd-instinct-mi200-devices
-rtol = 1e-2 if is_hip_mi200() else 0
-if torch.allclose(triton_output, torch_output, atol=1e-2, rtol=rtol):
+
+forward_error = estimate_matmul_error(a, b)
+compute_error = torch.norm(triton_output.to(torch.float64) - torch_output.to(torch.float64))
+
+if compute_error < forward_error:
     print("✅ Triton and Torch match")
 else:
     print("❌ Triton and Torch differ")