diff --git a/vllm/attention/backends/rocm_flash_attn.py b/vllm/attention/backends/rocm_flash_attn.py
index 45102c655d0bf..5f6e7c7cca94d 100644
--- a/vllm/attention/backends/rocm_flash_attn.py
+++ b/vllm/attention/backends/rocm_flash_attn.py
@@ -681,9 +681,8 @@ def forward(
                             seq_lens,
                             make_attn_mask=False)  # type: ignore
                     full_scales = (
-                        1.0 / layer._q_scale.item(),
-                        1.0 / layer._k_scale.item(), 1.0 /
-                        layer._v_scale.item(), 1.0 / layer._prob_scale.item(),
+                        layer._q_scale.item(), layer._k_scale.item(),
+                        layer._v_scale.item(), layer._prob_scale.item(),
                         fp8_out_scale.item()) if (
                             fp8_out_scale and layer._q_scale
                             and layer._prob_scale
diff --git a/vllm/attention/ops/triton_flash_attention.py b/vllm/attention/ops/triton_flash_attention.py
index 828bdc2905957..bc550a85f5a92 100644
--- a/vllm/attention/ops/triton_flash_attention.py
+++ b/vllm/attention/ops/triton_flash_attention.py
@@ -390,6 +390,8 @@ def get_autotune_configs():
 
 autotune_configs, autotune_keys = get_autotune_configs()
 
+float8_info = torch.finfo(torch.float8_e4m3fnuz)
+
 
 @triton.autotune(
     configs=autotune_configs,
@@ -451,6 +453,8 @@ def attn_fwd(
     BIAS_TYPE: tl.constexpr,
     ENABLE_DROPOUT: tl.constexpr,
     RETURN_ENCODED_SOFTMAX: tl.constexpr,
+    FP8_MIN: tl.constexpr = float8_info.min,
+    FP8_MAX: tl.constexpr = float8_info.max,
 ):
     start_m = tl.program_id(0)
     off_h_q = tl.program_id(1)
@@ -733,6 +737,7 @@ def attn_fwd(
     causal_start_idx = seqlen_q - seqlen_k
     if USE_FP8:
         acc *= o_descale
+        acc = tl.clamp(acc, FP8_MIN, FP8_MAX)
     acc = acc.to(Out.type.element_ty)
     if IS_CAUSAL:  # noqa: SIM102
         if causal_start_idx > start_m_idx and causal_start_idx < end_m_idx:
@@ -832,9 +837,9 @@ def forward(
 
             def check_and_convert(t, scale):
                 if t.dtype != float8:
-                    finfo = torch.finfo(float8)
                     descale = 1.0 / scale
-                    ts = (t * descale).clamp(min=finfo.min, max=finfo.max)
+                    ts = (t * descale).clamp(min=float8_info.min,
+                                             max=float8_info.max)
                     return ts.to(float8)
                 else:
                     return t