diff --git a/vllm/model_executor/warmup/kernel_warmup.py b/vllm/model_executor/warmup/kernel_warmup.py
index 95f5982bc8c7..a1a0feab9b4c 100644
--- a/vllm/model_executor/warmup/kernel_warmup.py
+++ b/vllm/model_executor/warmup/kernel_warmup.py
@@ -73,6 +73,55 @@ def _is_flashinfer_backend(backend):
             create_mixed_batch=True,
         )
 
+    # Attention backend warmup (non-FlashInfer too).
+    #
+    # NOTE: The default startup "profile run" intentionally avoids building
+    # attention metadata unless running FULL CUDA graphs, which can leave
+    # attention kernels/backends (e.g. FlashAttention/Triton) uninitialized.
+    # That shows up as a slow first real request due to JIT/autotune work.
+    #
+    # Here we force a small attention-inclusive dummy run so the attention path
+    # is exercised during startup across backends.
+    if (
+        (not worker.model_runner.is_pooling_model)
+        and worker.model_runner.attn_groups
+        and (not worker.model_config.is_encoder_decoder)
+    ):
+        try:
+            max_batched_tokens = worker.scheduler_config.max_num_batched_tokens
+            max_num_seqs = worker.scheduler_config.max_num_seqs
+            uniform_decode_query_len = getattr(
+                worker.model_runner, "uniform_decode_query_len", 1
+            )
+
+            # 1) Mixed prefill+decode warmup (covers both paths, low cost).
+            mixed_tokens = min(16, max_batched_tokens)
+            if mixed_tokens > 0:
+                worker.model_runner._dummy_run(
+                    num_tokens=mixed_tokens,
+                    skip_eplb=True,
+                    is_profile=True,
+                    force_attention=True,
+                    create_mixed_batch=True,
+                )
+
+            # 2) Uniform decode warmup (hits decode-specialized attention paths).
+            # Keep it small to avoid extra startup latency.
+            q = max(1, int(uniform_decode_query_len))
+            num_reqs = min(16, max_num_seqs, max_batched_tokens // q)
+            decode_tokens = q * num_reqs
+            if decode_tokens > 0:
+                worker.model_runner._dummy_run(
+                    num_tokens=decode_tokens,
+                    skip_eplb=True,
+                    is_profile=True,
+                    force_attention=True,
+                    uniform_decode=True,
+                )
+        except Exception:
+            # Best-effort: warmup should never block engine startup.
+            logger.exception("Attention backend warmup failed; continuing startup.")
+
 
 def flashinfer_autotune(runner: "GPUModelRunner") -> None:
     """