diff --git a/python/sglang/bench_one_batch.py b/python/sglang/bench_one_batch.py
index b462bb7d2..6c845af1d 100644
--- a/python/sglang/bench_one_batch.py
+++ b/python/sglang/bench_one_batch.py
@@ -92,11 +92,25 @@ from sglang.srt.utils.hf_transformers_utils import get_tokenizer
 from sglang.srt.utils.tensor_bridge import use_mlx
 
 
-def start_profile(profile_activities, profile_record_shapes=False, rank_print=print):
+def start_profile(
+    profile_activities,
+    profile_record_shapes=False,
+    rank_print=print,
+    trace_filename=None,
+):
     """
     Abstracted function to start profiling based on profile_activities.
     Returns profiler object (or None).
     """
+    if use_mlx():
+        import mlx.core as mx
+
+        if trace_filename:
+            mlx_trace_filename = trace_filename.replace(".trace.json.gz", ".gputrace")
+            mx.metal.start_capture(mlx_trace_filename)
+            rank_print(f"MLX Metal capture started directly to {mlx_trace_filename}")
+        return "mlx"
+
     if "CUDA_PROFILER" in profile_activities:
         try:
             torch.cuda.cudart().cudaProfilerStart()
@@ -135,6 +149,19 @@ def stop_profile(
     Abstracted function to stop profiling based on profile_activities.
     Optionally saves trace results and prints completion messages.
     """
+    if profiler == "mlx":
+        import mlx.core as mx
+
+        mx.metal.stop_capture()
+
+        if save_trace and trace_filename:
+            # Change SGLang's default torch extension to Apple's .gputrace extension
+            mlx_trace_filename = trace_filename.replace(".trace.json.gz", ".gputrace")
+
+            stage_desc = f"for {stage}" if stage else ""
+            rank_print(f"MLX Metal gputrace {stage_desc} saved to {mlx_trace_filename}")
+        return
+
     if "CUDA_PROFILER" in profile_activities:
         try:
             torch.cuda.cudart().cudaProfilerStop()
@@ -659,11 +686,16 @@ def latency_test_run_once(
 
     profiler = None
     enable_profile_prefill = profile and profile_stage in ["all", "prefill"]
+    trace_filename_prefill = None
     if enable_profile_prefill:
+        trace_filename_prefill = _create_torch_profiler_filename(
+            profile_filename_prefix, batch_size, input_len, output_len, "prefill"
+        )
         profiler = start_profile(
             profile_activities,
             profile_record_shapes=profile_record_shapes,
             rank_print=rank_print,
+            trace_filename=trace_filename_prefill,  # pass it in here for the MLX path only
         )
 
     model_runner.synchronize()
@@ -673,15 +705,12 @@ def latency_test_run_once(
     prefill_latency = time.perf_counter() - tic
 
     if enable_profile_prefill:
-        trace_filename = _create_torch_profiler_filename(
-            profile_filename_prefix, batch_size, input_len, output_len, "prefill"
-        )
         stop_profile(
             profiler,
             profile_activities,
             rank_print=rank_print,
             save_trace=True,
-            trace_filename=trace_filename,
+            trace_filename=trace_filename_prefill,
             stage="prefill",
         )
 
@@ -700,15 +729,20 @@ def latency_test_run_once(
     )
     profile_end = profile_start + (profile_steps if profile_steps is not None else 1)
     enable_profile_decode = profile and profile_stage in ["all", "decode"]
+    trace_filename_decode = None
     profiler = None
     for i in range(output_len - 1):
         model_runner.synchronize()
         # Start profiler at the specified step
         if enable_profile_decode and i == profile_start:
+            trace_filename_decode = _create_torch_profiler_filename(
+                profile_filename_prefix, batch_size, input_len, output_len, "decode"
+            )
             profiler = start_profile(
                 profile_activities,
                 profile_record_shapes=profile_record_shapes,
                 rank_print=rank_print,
+                trace_filename=trace_filename_decode,
             )
 
         tic = time.perf_counter()
@@ -718,15 +752,12 @@ def latency_test_run_once(
 
         # Stop profiler after the specified number of steps
         if enable_profile_decode and profiler is not None and i >= profile_end - 1:
-            trace_filename = _create_torch_profiler_filename(
-                profile_filename_prefix, batch_size, input_len, output_len, "decode"
-            )
             stop_profile(
                 profiler,
                 profile_activities,
                 rank_print=rank_print,
                 save_trace=True,
-                trace_filename=trace_filename,
+                trace_filename=trace_filename_decode,
                 stage="decode",
             )
             profiler = None