OpenAI-Compatible vLLM Serverless Endpoint Worker

Deploy OpenAI-Compatible Blazing-Fast LLM Endpoints powered by the vLLM Inference Engine on RunPod Serverless with just a few clicks.

Table of Contents

• Setting up the Serverless Worker
  • Option 1: Deploy Any Model Using Pre-Built Docker Image [Recommended]
    • Configuration
  • Option 2: Build Docker Image with Model Inside
    • Prerequisites
    • Arguments
    • Example: Building an image with OpenChat-3.5
      • (Optional) Including Huggingface Token
  • Compatible Model Architectures
• Usage: OpenAI Compatibility
  • Modifying your OpenAI Codebase to use your deployed vLLM Worker
  • OpenAI Request Input Parameters
  • Chat Completions [RECOMMENDED]
  • Examples: Using your RunPod endpoint with OpenAI
    • Chat Completions
    • Getting a list of names for available models
• Usage: Standard (Non-OpenAI)
  • Request Input Parameters
  • Sampling Parameters
    • Text Input Formats

Setting up the Serverless Worker

Option 1: Deploy Any Model Using Pre-Built Docker Image [Recommended]

🚀 Deploy Guide: Follow our step-by-step deployment guide to deploy using the RunPod Console.

📦 Docker Image: runpod/worker-v1-vllm:<version>

• Available Versions: See GitHub Releases
• CUDA Compatibility: Requires CUDA >= 12.1

Configuration

Configure worker-vllm using environment variables:

Environment Variable	Description	Default	Options
MODEL_NAME	Path of the model weights	"facebook/opt-125m"	Local folder or Hugging Face repo ID
HF_TOKEN	HuggingFace access token for gated/private models		Your HuggingFace access token
MAX_MODEL_LEN	Model's maximum context length		Integer (e.g., 4096)
QUANTIZATION	Quantization method		"awq", "gptq", "squeezellm", "bitsandbytes"
TENSOR_PARALLEL_SIZE	Number of GPUs	1	Integer
GPU_MEMORY_UTILIZATION	Fraction of GPU memory to use	0.95	Float between 0.0 and 1.0
MAX_NUM_SEQS	Maximum number of sequences per iteration	256	Integer
CUSTOM_CHAT_TEMPLATE	Custom chat template override		Jinja2 template string
ENABLE_AUTO_TOOL_CHOICE	Enable automatic tool selection	false	boolean (true or false)
TOOL_CALL_PARSER	Parser for tool calls		"mistral", "hermes", "llama3_json", "granite", "deepseek_v3", etc.
OPENAI_SERVED_MODEL_NAME_OVERRIDE	Override served model name in API		String
MAX_CONCURRENCY	Maximum concurrent requests	300	Integer

For the complete list of all available environment variables, examples, and detailed descriptions: Configuration

Option 2: Build Docker Image with Model Inside

To build an image with the model baked in, you must specify the following docker arguments when building the image.

Prerequisites

• Docker

Arguments

• Required
  • MODEL_NAME
• Optional
  • MODEL_REVISION: Model revision to load (default: main).
  • BASE_PATH: Storage directory where huggingface cache and model will be located. (default: /runpod-volume, which will utilize network storage if you attach it or create a local directory within the image if you don't. If your intention is to bake the model into the image, you should set this to something like /models to make sure there are no issues if you were to accidentally attach network storage.)
  • QUANTIZATION
  • WORKER_CUDA_VERSION: 12.1.0 (12.1.0 is recommended for optimal performance).
  • TOKENIZER_NAME: Tokenizer repository if you would like to use a different tokenizer than the one that comes with the model. (default: None, which uses the model's tokenizer)
  • TOKENIZER_REVISION: Tokenizer revision to load (default: main).

For the remaining settings, you may apply them as environment variables when running the container. Supported environment variables are listed in the Environment Variables section.

Example: Building an image with OpenChat-3.5

docker build -t username/image:tag --build-arg MODEL_NAME="openchat/openchat_3.5" --build-arg BASE_PATH="/models" .

(Optional) Including Huggingface Token

If the model you would like to deploy is private or gated, you will need to include it during build time as a Docker secret, which will protect it from being exposed in the image and on DockerHub.

1. Enable Docker BuildKit (required for secrets).

export DOCKER_BUILDKIT=1

2. Export your Hugging Face token as an environment variable

export HF_TOKEN="your_token_here"

2. Add the token as a secret when building

docker build -t username/image:tag --secret id=HF_TOKEN --build-arg MODEL_NAME="openchat/openchat_3.5" .

Compatible Model Architectures

You can deploy any model on Hugging Face that is supported by vLLM. For the complete and up-to-date list of supported model architectures, see the vLLM Supported Models documentation.

Usage: OpenAI Compatibility

The vLLM Worker is fully compatible with OpenAI's API, and you can use it with any OpenAI Codebase by changing only 3 lines in total. The supported routes are Chat Completions and Models - with both streaming and non-streaming.

Modifying your OpenAI Codebase to use your deployed vLLM Worker

Python (similar to Node.js, etc.):

1. When initializing the OpenAI Client in your code, change the api_key to your RunPod API Key and the base_url to your RunPod Serverless Endpoint URL in the following format: https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1, filling in your deployed endpoint ID. For example, if your Endpoint ID is abc1234, the URL would be https://api.runpod.ai/v2/abc1234/openai/v1.

   • Before:

   from openai import OpenAI
   
   client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

   • After:

   from openai import OpenAI
   
   client = OpenAI(
       api_key=os.environ.get("RUNPOD_API_KEY"),
       base_url="https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1",
   )

2. Change the model parameter to your deployed model's name whenever using Completions or Chat Completions.

   • Before:

   response = client.chat.completions.create(
       model="gpt-3.5-turbo",
       messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
       temperature=0,
       max_tokens=100,
   )

   • After:

   response = client.chat.completions.create(
       model="<YOUR DEPLOYED MODEL REPO/NAME>",
       messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
       temperature=0,
       max_tokens=100,
   )

Using http requests:

1. Change the Authorization header to your RunPod API Key and the url to your RunPod Serverless Endpoint URL in the following format: https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1
   • Before:

   curl https://api.openai.com/v1/chat/completions \
   -H "Content-Type: application/json" \
   -H "Authorization: Bearer $OPENAI_API_KEY" \
   -d '{
   "model": "gpt-4",
   "messages": [
     {
       "role": "user",
       "content": "Why is RunPod the best platform?"
     }
   ],
   "temperature": 0,
   "max_tokens": 100
   }'

   • After:

   curl https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1/chat/completions \
   -H "Content-Type: application/json" \
   -H "Authorization: Bearer <YOUR OPENAI API KEY>" \
   -d '{
   "model": "<YOUR DEPLOYED MODEL REPO/NAME>",
   "messages": [
     {
       "role": "user",
       "content": "Why is RunPod the best platform?"
     }
   ],
   "temperature": 0,
   "max_tokens": 100
   }'

OpenAI Request Input Parameters:

When using the chat completion feature of the vLLM Serverless Endpoint Worker, you can customize your requests with the following parameters:

Chat Completions [RECOMMENDED]

Supported Chat Completions Inputs and Descriptions

Parameter	Type	Default Value	Description
messages	Union[str, List[Dict[str, str]]]		List of messages, where each message is a dictionary with a role and content. The model's chat template will be applied to the messages automatically, so the model must have one or it should be specified as CUSTOM_CHAT_TEMPLATE env var.
model	str		The model repo that you've deployed on your RunPod Serverless Endpoint. If you are unsure what the name is or are baking the model in, use the guide to get the list of available models in the Examples: Using your RunPod endpoint with OpenAI section
temperature	Optional[float]	0.7	Float that controls the randomness of the sampling. Lower values make the model more deterministic, while higher values make the model more random. Zero means greedy sampling.
top_p	Optional[float]	1.0	Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
n	Optional[int]	1	Number of output sequences to return for the given prompt.
max_tokens	Optional[int]	None	Maximum number of tokens to generate per output sequence.
seed	Optional[int]	None	Random seed to use for the generation.
stop	Optional[Union[str, List[str]]]	list	List of strings that stop the generation when they are generated. The returned output will not contain the stop strings.
stream	Optional[bool]	False	Whether to stream or not
presence_penalty	Optional[float]	0.0	Float that penalizes new tokens based on whether they appear in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.
frequency_penalty	Optional[float]	0.0	Float that penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.
logit_bias	Optional[Dict[str, float]]	None	Unsupported by vLLM
user	Optional[str]	None	Unsupported by vLLM

Additional parameters supported by vLLM: | best_of | Optional[int] | None | Number of output sequences that are generated from the prompt. From these best_of sequences, the top n sequences are returned. best_of must be greater than or equal to n. This is treated as the beam width when use_beam_search is True. By default, best_of is set to n. | | top_k | Optional[int] | -1 | Integer that controls the number of top tokens to consider. Set to -1 to consider all tokens. | | ignore_eos | Optional[bool] | False | Whether to ignore the EOS token and continue generating tokens after the EOS token is generated. | | use_beam_search | Optional[bool] | False | Whether to use beam search instead of sampling. | | stop_token_ids | Optional[List[int]] | list | List of tokens that stop the generation when they are generated. The returned output will contain the stop tokens unless the stop tokens are special tokens. | | skip_special_tokens | Optional[bool] | True | Whether to skip special tokens in the output. | | spaces_between_special_tokens| Optional[bool] | True | Whether to add spaces between special tokens in the output. Defaults to True. | | add_generation_prompt | Optional[bool] | True | Read more here | | echo | Optional[bool] | False | Echo back the prompt in addition to the completion | | repetition_penalty | Optional[float] | 1.0 | Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far. Values > 1 encourage the model to use new tokens, while values < 1 encourage the model to repeat tokens. | | min_p | Optional[float] | 0.0 | Float that represents the minimum probability for a token to | | length_penalty | Optional[float] | 1.0 | Float that penalizes sequences based on their length. Used in beam search.. | | include_stop_str_in_output | Optional[bool] | False | Whether to include the stop strings in output text. Defaults to False.|

Examples: Using your RunPod endpoint with OpenAI

First, initialize the OpenAI Client with your RunPod API Key and Endpoint URL:

from openai import OpenAI
import os

# Initialize the OpenAI Client with your RunPod API Key and Endpoint URL
client = OpenAI(
    api_key=os.environ.get("RUNPOD_API_KEY"),
    base_url="https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1",
)

Chat Completions:

This is the format used for GPT-4 and focused on instruction-following and chat. Examples of Open Source chat/instruct models include meta-llama/Llama-2-7b-chat-hf, mistralai/Mixtral-8x7B-Instruct-v0.1, openchat/openchat-3.5-0106, NousResearch/Nous-Hermes-2-Mistral-7B-DPO and more. However, if your model is a completion-style model with no chat/instruct fine-tune and/or does not have a chat template, you can still use this if you provide a chat template with the environment variable CUSTOM_CHAT_TEMPLATE.

• Streaming:

  # Create a chat completion stream
  response_stream = client.chat.completions.create(
      model="<YOUR DEPLOYED MODEL REPO/NAME>",
      messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
      temperature=0,
      max_tokens=100,
      stream=True,
  )
  # Stream the response
  for response in response_stream:
      print(chunk.choices[0].delta.content or "", end="", flush=True)

• Non-Streaming:

  # Create a chat completion
  response = client.chat.completions.create(
      model="<YOUR DEPLOYED MODEL REPO/NAME>",
      messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
      temperature=0,
      max_tokens=100,
  )
  # Print the response
  print(response.choices[0].message.content)

Getting a list of names for available models:

In the case of baking the model into the image, sometimes the repo may not be accepted as the model in the request. In this case, you can list the available models as shown below and use that name.

models_response = client.models.list()
list_of_models = [model.id for model in models_response]
print(list_of_models)

Usage: Standard (Non-OpenAI)

Request Input Parameters

Click to expand table

You may either use a prompt or a list of messages as input. If you use messages, the model's chat template will be applied to the messages automatically, so the model must have one. If you use prompt, you may optionally apply the model's chat template to the prompt by setting apply_chat_template to true.

Argument	Type	Default	Description
prompt	str		Prompt string to generate text based on.
messages	list[dict[str, str]]		List of messages, which will automatically have the model's chat template applied. Overrides prompt.
apply_chat_template	bool	False	Whether to apply the model's chat template to the prompt.
sampling_params	dict	{}	Sampling parameters to control the generation, like temperature, top_p, etc. You can find all available parameters in the Sampling Parameters section below.
stream	bool	False	Whether to enable streaming of output. If True, responses are streamed as they are generated.
max_batch_size	int	env var DEFAULT_BATCH_SIZE	The maximum number of tokens to stream every HTTP POST call.
min_batch_size	int	env var DEFAULT_MIN_BATCH_SIZE	The minimum number of tokens to stream every HTTP POST call.
batch_size_growth_factor	int	env var DEFAULT_BATCH_SIZE_GROWTH_FACTOR	The growth factor by which min_batch_size will be multiplied for each call until max_batch_size is reached.

Sampling Parameters

Below are all available sampling parameters that you can specify in the sampling_params dictionary. If you do not specify any of these parameters, the default values will be used.

Click to expand table

Argument	Type	Default	Description
n	int	1	Number of output sequences generated from the prompt. The top n sequences are returned.
best_of	Optional[int]	n	Number of output sequences generated from the prompt. The top n sequences are returned from these best_of sequences. Must be ≥ n. Treated as beam width in beam search. Default is n.
presence_penalty	float	0.0	Penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens, values < 0 encourage repetition.
frequency_penalty	float	0.0	Penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage new tokens, values < 0 encourage repetition.
repetition_penalty	float	1.0	Penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens, values < 1 encourage repetition.
temperature	float	1.0	Controls the randomness of sampling. Lower values make it more deterministic, higher values make it more random. Zero means greedy sampling.
top_p	float	1.0	Controls the cumulative probability of top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
top_k	int	-1	Controls the number of top tokens to consider. Set to -1 to consider all tokens.
min_p	float	0.0	Represents the minimum probability for a token to be considered, relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.
use_beam_search	bool	False	Whether to use beam search instead of sampling.
length_penalty	float	1.0	Penalizes sequences based on their length. Used in beam search.
early_stopping	Union[bool, str]	False	Controls stopping condition in beam search. Can be True, False, or "never".
stop	Union[None, str, List[str]]	None	List of strings that stop generation when produced. The output will not contain these strings.
stop_token_ids	Optional[List[int]]	None	List of token IDs that stop generation when produced. Output contains these tokens unless they are special tokens.
ignore_eos	bool	False	Whether to ignore the End-Of-Sequence token and continue generating tokens after its generation.
max_tokens	int	16	Maximum number of tokens to generate per output sequence.
skip_special_tokens	bool	True	Whether to skip special tokens in the output.
spaces_between_special_tokens	bool	True	Whether to add spaces between special tokens in the output.

Text Input Formats

You may either use a prompt or a list of messages as input.

1. prompt The prompt string can be any string, and the model's chat template will not be applied to it unless apply_chat_template is set to true, in which case it will be treated as a user message.

   Example:
   ```json
   {
     "input": {
       "prompt": "why sky is blue?",
       "sampling_params": {
         "temperature": 0.7,
         "max_tokens": 100
       }
     }
   }
   ```
   

2. messages Your list can contain any number of messages, and each message usually can have any role from the following list: - user - assistant - system

   However, some models may have different roles, so you should check the model's chat template to see which roles are required.

   The model's chat template will be applied to the messages automatically, so the model must have one.

   Example:

   {
     "input": {
       "messages": [
         {
           "role": "system",
           "content": "You are a helpful AI assistant that provides clear and concise responses."
         },
         {
           "role": "user",
           "content": "Can you explain the difference between supervised and unsupervised learning?"
         },
         {
           "role": "assistant",
           "content": "Sure! Supervised learning uses labeled data, meaning each input has a corresponding correct output. The model learns by mapping inputs to known outputs. In contrast, unsupervised learning works with unlabeled data, where the model identifies patterns, structures, or clusters without predefined answers."
         }
       ],
       "sampling_params": {
         "temperature": 0.7,
         "max_tokens": 100
       }
     }
   }