diff --git a/tritonbench/operators/gdpa/math.py b/tritonbench/operators/gdpa/math.py
index 7fedaf29..0151de7f 100644
--- a/tritonbench/operators/gdpa/math.py
+++ b/tritonbench/operators/gdpa/math.py
@@ -28,6 +28,11 @@
         from triton.language.math import fast_dividef, fast_expf
 
 
+HAS_FAST_TANH_INSTRUCTION = (
+    torch.version.cuda is not None and torch.cuda.get_device_capability()[0] >= 9
+)  # H100
+
+
 # Don't change the order of the enum values, as they are used to index
 # Only add new activation functions at the end of the enum
 class Activation(str, Enum):
@@ -50,17 +55,6 @@ def activation_string_to_int(s: str):
     return activation_to_int.get(enum_val)
 
 
-def is_hip_or_a100():
-    try:
-        if triton.runtime.driver.active.get_current_target().backend == "hip":
-            return True
-        elif torch.cuda.get_device_capability()[0] < 9:  # A100
-            return True
-        return False
-    except Exception:
-        return False
-
-
 @triton.jit
 def tanh(x):
     # Tanh is just a scaled sigmoid
@@ -79,11 +73,11 @@ def gelu_grad(x):
     return cdf + x * pdf
 
 
-if is_hip_or_a100():
-    # For AMD or A100, use tanh as a fallback
+if not HAS_FAST_TANH_INSTRUCTION:
+
     @triton.jit
     def tanh_approx_fp32(x):
-        return tanh(x)
+        return 2 * fast_dividef(1.0, 1.0 + fast_expf(-2.0 * x)) - 1.0
 
 else: