diff --git a/vllm/entrypoints/cli/main.py b/vllm/entrypoints/cli/main.py
index 13f2761b0db06..aa54bd66bed67 100644
--- a/vllm/entrypoints/cli/main.py
+++ b/vllm/entrypoints/cli/main.py
@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
 # The CLI entrypoint to vLLM.
-import os
 import signal
 import sys
 
@@ -9,11 +8,9 @@ import vllm.entrypoints.cli.benchmark.main
 import vllm.entrypoints.cli.openai
 import vllm.entrypoints.cli.serve
 import vllm.version
-from vllm.logger import init_logger
+from vllm.entrypoints.utils import cli_env_setup
 from vllm.utils import FlexibleArgumentParser
 
-logger = init_logger(__name__)
-
 CMD_MODULES = [
     vllm.entrypoints.cli.openai,
     vllm.entrypoints.cli.serve,
@@ -30,29 +27,8 @@ def register_signal_handlers():
     signal.signal(signal.SIGTSTP, signal_handler)
 
 
-def env_setup():
-    # The safest multiprocessing method is `spawn`, as the default `fork` method
-    # is not compatible with some accelerators. The default method will be
-    # changing in future versions of Python, so we should use it explicitly when
-    # possible.
-    #
-    # We only set it here in the CLI entrypoint, because changing to `spawn`
-    # could break some existing code using vLLM as a library. `spawn` will cause
-    # unexpected behavior if the code is not protected by
-    # `if __name__ == "__main__":`.
-    #
-    # References:
-    # - https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
-    # - https://pytorch.org/docs/stable/notes/multiprocessing.html#cuda-in-multiprocessing
-    # - https://pytorch.org/docs/stable/multiprocessing.html#sharing-cuda-tensors
-    # - https://docs.habana.ai/en/latest/PyTorch/Getting_Started_with_PyTorch_and_Gaudi/Getting_Started_with_PyTorch.html?highlight=multiprocessing#torch-multiprocessing-for-dataloaders
-    if "VLLM_WORKER_MULTIPROC_METHOD" not in os.environ:
-        logger.debug("Setting VLLM_WORKER_MULTIPROC_METHOD to 'spawn'")
-        os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-
-
 def main():
-    env_setup()
+    cli_env_setup()
 
     parser = FlexibleArgumentParser(description="vLLM CLI")
     parser.add_argument('-v',
diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py
index 18d75a04ab0f3..2a61259896a37 100644
--- a/vllm/entrypoints/openai/api_server.py
+++ b/vllm/entrypoints/openai/api_server.py
@@ -82,7 +82,8 @@ from vllm.entrypoints.openai.serving_tokenization import (
 from vllm.entrypoints.openai.serving_transcription import (
     OpenAIServingTranscription)
 from vllm.entrypoints.openai.tool_parsers import ToolParserManager
-from vllm.entrypoints.utils import load_aware_call, with_cancellation
+from vllm.entrypoints.utils import (cli_env_setup, load_aware_call,
+                                    with_cancellation)
 from vllm.logger import init_logger
 from vllm.reasoning import ReasoningParserManager
 from vllm.transformers_utils.config import (
@@ -1106,6 +1107,7 @@ if __name__ == "__main__":
     # NOTE(simon):
     # This section should be in sync with vllm/entrypoints/cli/main.py for CLI
     # entrypoints.
+    cli_env_setup()
     parser = FlexibleArgumentParser(
         description="vLLM OpenAI-Compatible RESTful API server.")
     parser = make_arg_parser(parser)
diff --git a/vllm/entrypoints/utils.py b/vllm/entrypoints/utils.py
index 773f52fa38f88..b88c2b3a080fd 100644
--- a/vllm/entrypoints/utils.py
+++ b/vllm/entrypoints/utils.py
@@ -2,11 +2,16 @@
 
 import asyncio
 import functools
+import os
 
 from fastapi import Request
 from fastapi.responses import JSONResponse, StreamingResponse
 from starlette.background import BackgroundTask, BackgroundTasks
 
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
 
 async def listen_for_disconnect(request: Request) -> None:
     """Returns if a disconnect message is received"""
@@ -108,3 +113,24 @@ def load_aware_call(func):
         return response
 
     return wrapper
+
+
+def cli_env_setup():
+    # The safest multiprocessing method is `spawn`, as the default `fork` method
+    # is not compatible with some accelerators. The default method will be
+    # changing in future versions of Python, so we should use it explicitly when
+    # possible.
+    #
+    # We only set it here in the CLI entrypoint, because changing to `spawn`
+    # could break some existing code using vLLM as a library. `spawn` will cause
+    # unexpected behavior if the code is not protected by
+    # `if __name__ == "__main__":`.
+    #
+    # References:
+    # - https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
+    # - https://pytorch.org/docs/stable/notes/multiprocessing.html#cuda-in-multiprocessing
+    # - https://pytorch.org/docs/stable/multiprocessing.html#sharing-cuda-tensors
+    # - https://docs.habana.ai/en/latest/PyTorch/Getting_Started_with_PyTorch_and_Gaudi/Getting_Started_with_PyTorch.html?highlight=multiprocessing#torch-multiprocessing-for-dataloaders
+    if "VLLM_WORKER_MULTIPROC_METHOD" not in os.environ:
+        logger.debug("Setting VLLM_WORKER_MULTIPROC_METHOD to 'spawn'")
+        os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"