diff --git a/python/sglang/srt/model_executor/model_runner.py b/python/sglang/srt/model_executor/model_runner.py
index 8b1de56b8..e94ca7318 100644
--- a/python/sglang/srt/model_executor/model_runner.py
+++ b/python/sglang/srt/model_executor/model_runner.py
@@ -15,6 +15,7 @@
 
 from __future__ import annotations
 
+import contextlib
 import datetime
 import gc
 import inspect
@@ -2902,10 +2903,18 @@ class ModelRunner(ModelRunnerKVCacheMixin):
     ) -> ModelRunnerOutput:
         self.forward_pass_id += 1
 
-        with get_global_expert_distribution_recorder().with_forward_pass(
-            self.forward_pass_id,
-            forward_batch,
-        ) as recorder_outputs:
+        step_span_ctx = (
+            torch.profiler.record_function(_build_step_span_name(forward_batch))
+            if torch.autograd._profiler_enabled()
+            else contextlib.nullcontext()
+        )
+        with (
+            step_span_ctx,
+            get_global_expert_distribution_recorder().with_forward_pass(
+                self.forward_pass_id,
+                forward_batch,
+            ) as recorder_outputs,
+        ):
             output = self._forward_raw(
                 forward_batch,
                 skip_attn_backend_init,
@@ -3211,6 +3220,39 @@ def _unwrap_tensor(tensor, tp_rank, device):
     return tensor.to(device)
 
 
+def _build_step_span_name(forward_batch: ForwardBatch) -> str:
+    """Build a profile-trace span name for one forward step.
+
+    Format:
+      step[decode bs=N]                   — decode-only batch
+      step[prefill bs=N toks=T]           — extend-only (prefill) batch
+      step[mixed bs=N ext=T dec=D]        — extend+decode mixed batch
+      step[idle]                          — idle/padding step
+      step[<MODE> bs=N]                   — other modes (target-verify, etc.)
+
+    Used by ModelRunner.forward to wrap each step in a torch.profile
+    record_function so Chrome traces show labeled step boundaries.
+    """
+    mode = forward_batch.forward_mode
+    bs = forward_batch.batch_size
+    if mode.is_idle():
+        return "step[idle]"
+    if mode.is_decode():
+        return f"step[decode bs={bs}]"
+    if mode.is_extend():
+        ext_toks = forward_batch.extend_num_tokens or 0
+        ext_seqs = (
+            forward_batch.extend_seq_lens.shape[0]
+            if forward_batch.extend_seq_lens is not None
+            else bs
+        )
+        dec_seqs = bs - ext_seqs
+        if dec_seqs > 0:
+            return f"step[mixed bs={bs} ext={ext_toks} dec={dec_seqs}]"
+        return f"step[prefill bs={bs} toks={ext_toks}]"
+    return f"step[{mode.name} bs={bs}]"
+
+
 @dataclass
 class LocalSerializedTensor:
     """torch.Tensor that gets serialized by MultiprocessingSerializer (which only serializes a pointer and not the data).