diff --git a/vllm/model_executor/layers/attention/mm_encoder_attention.py b/vllm/model_executor/layers/attention/mm_encoder_attention.py
index 1c9ee2bf5bfb..d0eae8f72814 100644
--- a/vllm/model_executor/layers/attention/mm_encoder_attention.py
+++ b/vllm/model_executor/layers/attention/mm_encoder_attention.py
@@ -529,6 +529,7 @@ def _forward_sdpa(
             num_kv_heads=self.num_kv_heads,
             dtype=query.dtype,
             is_causal=False,  # ViT attention is non-causal
+            v_token_stride=value.stride(1),
         ):
             output = vit_torch_sdpa_wrapper(
                 q=query,
@@ -582,6 +583,7 @@ def _forward_fa(
             num_kv_heads=self.num_kv_heads,
             dtype=query.dtype,
             is_causal=False,  # ViT attention is non-causal
+            v_token_stride=value.stride(1),
         ):
             output = vit_flash_attn_wrapper(
                 q=query,
@@ -633,6 +635,7 @@ def _forward_triton(
             num_kv_heads=self.num_kv_heads,
             dtype=query.dtype,
             is_causal=False,  # ViT attention is non-causal
+            v_token_stride=value.stride(1),
         ):
             output = vit_triton_attn_wrapper(
                 q=query,
diff --git a/vllm/v1/attention/ops/vit_attn_wrappers.py b/vllm/v1/attention/ops/vit_attn_wrappers.py
index c1274377b6e1..391b788a25b9 100644
--- a/vllm/v1/attention/ops/vit_attn_wrappers.py
+++ b/vllm/v1/attention/ops/vit_attn_wrappers.py
@@ -71,6 +71,7 @@ def flash_attn_maxseqlen_wrapper(
         num_kv_heads=num_kv_heads,
         dtype=q.dtype,
         is_causal=False,
+        v_token_stride=v.stride(0),
     ):
         output = flash_attn_varlen_func(
             q,
@@ -170,6 +171,7 @@ def triton_attn_wrapper(
         num_kv_heads=num_kv_heads,
         dtype=q.dtype,
         is_causal=False,
+        v_token_stride=v.stride(0),
     ):
         context_attention_fwd(
             q,
diff --git a/vllm/v1/utils.py b/vllm/v1/utils.py
index b089e551cbea..7ee12f56c456 100644
--- a/vllm/v1/utils.py
+++ b/vllm/v1/utils.py
@@ -707,6 +707,7 @@ def create_attention_profiler_scope(
     num_kv_heads: int,
     dtype: torch.dtype,
     is_causal: bool,
+    v_token_stride: int | None = None,
 ) -> AbstractContextManager:
     """Create a profiler scope for attention operations with metadata.
 
@@ -720,6 +721,13 @@ def create_attention_profiler_scope(
         num_kv_heads: Number of KV heads (for GQA/MQA)
         dtype: Data type of the tensors
         is_causal: Whether causal masking is applied
+        v_token_stride: Optional per-token stride of the V operand. Only
+            surfaced in the label when it is *not* the natural contiguous value
+            (num_kv_heads * head_size) -- i.e. the real ViT layout where V is a
+            non-contiguous slice of the fused QKV projection (token stride
+            mult*H*D, e.g. 3*H*D). This carries a measurable latency impact on
+            some GPUs (e.g. Strix Point cold cache); natural strides are omitted
+            to keep the scope label short.
 
     Returns:
         Context manager for the profiler scope
@@ -733,6 +741,13 @@ def create_attention_profiler_scope(
         f"H={num_heads} D={head_size} KV_H={num_kv_heads} "
         f"DT={dtype_str} BE={backend_name}"
     )
+    # Only surface the V operand token stride when it is *not* the natural
+    # contiguous value (num_kv_heads * head_size). The real ViT layout feeds a
+    # non-contiguous V sliced from the fused QKV projection (token stride
+    # mult*H*D), which is what we want visible in the profile; natural strides
+    # are omitted to keep the label short.
+    if v_token_stride is not None and v_token_stride != num_kv_heads * head_size:
+        scope_name += f" Vstride={v_token_stride}"
 
     return record_function_or_nullcontext(scope_name)