diff --git a/python/tutorials/06-fused-attention.py b/python/tutorials/06-fused-attention.py
index 449e89f93e29..6ad01cbadc83 100644
--- a/python/tutorials/06-fused-attention.py
+++ b/python/tutorials/06-fused-attention.py
@@ -17,16 +17,16 @@
 import triton
 import triton.language as tl
 
-torch_dtype:tl.constexpr = torch.float16
-TORCH_HAS_FP8 = False
-TORCH_HAS_FP8E5 = hasattr(torch, 'float8_e5m2')
-TORCH_HAS_FP8E5FNUZ = hasattr(torch, 'float8_e5m2fnuz')
-if TORCH_HAS_FP8E5:
-    torch_dtype:tl.constexpr = torch.float8_e5m2
-    TORCH_HAS_FP8 = True
-if TORCH_HAS_FP8E5FNUZ:
-    torch_dtype:tl.constexpr = torch.float8_e5m2fnuz
-    TORCH_HAS_FP8 = True
+# Pick the fp8 data type
+
+# AMD E4M3B8
+# Note: When picking this f8 data type, scaling is required when using f8
+# for the second gemm
+#TORCH_HAS_FP8E4B8 = hasattr(torch, 'float8_e4m3fnuz')
+
+# AMD E5M2B16
+TORCH_HAS_FP8E5B16 = hasattr(torch, 'float8_e5m2fnuz')
+
 
 @triton.jit
 def _attn_fwd_inner(acc, l_i, m_i, q,
@@ -555,7 +555,7 @@ def forward(ctx, q, k, v, causal, sm_scale):
         Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
         assert Lq == Lk and Lk == Lv
         assert Lk in {16, 32, 64, 128}
-        o = torch.empty_like(q)
+        o = torch.empty_like(q, dtype=v.dtype)
         if torch.version.hip is None:
             BLOCK_M = 128
             BLOCK_N = 64 if Lk <= 64 else 32
@@ -642,26 +642,33 @@ def backward(ctx, do):
 
 attention = _attention.apply
 
-
-@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD',
-                         [(4, 48, 1024, 64),
-                          (4, 48, 2048, 64),
-                          (4, 48, 4096, 64),
-                          (4, 48, 1024, 128),
-                          (4, 48, 2048, 128),
-                          (4, 48, 4096, 128),
-                          #(4, 48, 8192, 64),
-                          #(4, 48, 16384, 64)
-                          ])
+name_to_torch_types = {
+    'fp16': torch.float16,
+}
+
+if TORCH_HAS_FP8E5B16:
+    name_to_torch_types['fp8'] = torch.float8_e5m2fnuz
+
+@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD, dtype',
+[ (*shape, dtype)
+    for shape in [(4, 48, 1024, 64),
+                  (4, 48, 2048, 64),
+                  (4, 48, 4096, 64),
+                  (4, 48, 1024, 128),
+                  (4, 48, 2048, 128),
+                  (4, 48, 4096, 128)]
+    for dtype in ['fp16', 'fp8']])
 @pytest.mark.parametrize('causal', [False, True])
-def test_op_fwd(Z, H, N_CTX, D_HEAD, causal, dtype=torch.float16):
+def test_op_fwd(Z, H, N_CTX, D_HEAD, causal, dtype):
+    if dtype == 'fp8' and not TORCH_HAS_FP8E5B16:
+        pytest.skip("fp8 not supported")
     torch.manual_seed(20)
-    q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
-    k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
-    v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
-    if TORCH_HAS_FP8:
-        q = q.to(torch_dtype)
-        k = k.to(torch_dtype)
+    q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=torch.float16, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
+    k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=torch.float16, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
+    v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=torch.float16, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
+
+    q = q.to(name_to_torch_types[dtype])
+    k = k.to(name_to_torch_types[dtype])
     sm_scale = 0.5
     dout = torch.randn_like(q, dtype=torch.float16)
     # reference implementation
@@ -674,7 +681,9 @@ def test_op_fwd(Z, H, N_CTX, D_HEAD, causal, dtype=torch.float16):
     # triton implementation
     tri_out = attention(q, k, v, causal, sm_scale)
     # compare
-    torch.testing.assert_close(ref_out, tri_out, atol=1e-2, rtol=1e-2)
+    atol = 1.4e-1 if dtype == 'fp8' else 1e-2
+    rtol = 1e-2 if dtype == 'fp8' else 0
+    torch.testing.assert_close(ref_out, tri_out, atol=atol, rtol=rtol)
 
 
 @pytest.mark.parametrize('Z, H, N_CTX, D_HEAD',
@@ -775,9 +784,6 @@ def bench_flash_attention(BATCH, H, N_CTX, D_HEAD, causal, mode, provider, dtype
         q = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
         k = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
         v = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
-        if mode == "fwd" and TORCH_HAS_FP8:
-            q = q.to(torch_dtype)
-            k = k.to(torch_dtype)
         sm_scale = D_HEAD ** -0.5
         fn = lambda: attention(q, k, v, causal, sm_scale)
         if mode == 'bwd':