diff --git a/vllm/executor/ray_gpu_executor.py b/vllm/executor/ray_gpu_executor.py
index 5db2f3f652532..7aca5e36107aa 100644
--- a/vllm/executor/ray_gpu_executor.py
+++ b/vllm/executor/ray_gpu_executor.py
@@ -48,6 +48,21 @@ def _init_executor(self) -> None:
         if USE_RAY_COMPILED_DAG:
             self.forward_dag = self._compiled_ray_dag()
 
+    def _configure_ray_workers_use_nsight(self,
+                                          ray_remote_kwargs) -> Dict[str, Any]:
+        # If nsight profiling is enabled, we need to set the profiling
+        # configuration for the ray workers as runtime env.
+        runtime_env = ray_remote_kwargs.setdefault("runtime_env", {})
+        runtime_env.update({
+            "nsight": {
+                "t": "cuda,cudnn,cublas",
+                "o": "'worker_process_%p'",
+                "cuda-graph-trace": "node",
+            }
+        })
+
+        return ray_remote_kwargs
+
     def _init_workers_ray(self, placement_group: "PlacementGroup",
                           **ray_remote_kwargs):
         if self.parallel_config.tensor_parallel_size == 1:
@@ -63,6 +78,10 @@ def _init_workers_ray(self, placement_group: "PlacementGroup",
         # The remaining workers are the actual ray actors.
         self.workers: List[RayWorkerVllm] = []
 
+        if self.parallel_config.ray_workers_use_nsight:
+            ray_remote_kwargs = self._configure_ray_workers_use_nsight(
+                ray_remote_kwargs)
+
         # Create the workers.
         driver_ip = get_ip()
         for bundle_id, bundle in enumerate(placement_group.bundle_specs):