🦾 OpenLLM: Self-Hosting LLMs Made Easy

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OpenLLM allows developers to run **any open-source LLMs** (Llama 3.3, Qwen2.5, Phi3 and [more](#supported-models)) or **custom models** as **OpenAI-compatible APIs** with a single command. It features a [built-in chat UI](#chat-ui), state-of-the-art inference backends, and a simplified workflow for creating enterprise-grade cloud deployment with Docker, Kubernetes, and [BentoCloud](#deploy-to-bentocloud). Understand the [design philosophy of OpenLLM](https://www.bentoml.com/blog/from-ollama-to-openllm-running-llms-in-the-cloud). ## Get Started Run the following commands to install OpenLLM and explore it interactively. ```bash pip install openllm # or pip3 install openllm openllm hello ``` ![hello](https://github.com/user-attachments/assets/5af19f23-1b34-4c45-b1e0-a6798b4586d1) ## Supported models OpenLLM supports a wide range of state-of-the-art open-source LLMs. You can also add a [model repository to run custom models](#set-up-a-custom-repository) with OpenLLM.

Model	Parameters	Required GPU	Start a Server
deepseek	r1-671b	80Gx16	`openllm serve deepseek:r1-671b`
gemma2	2b	12G	`openllm serve gemma2:2b`
gemma3	3b	12G	`openllm serve gemma3:3b`
jamba1.5	mini-ff0a	80Gx2	`openllm serve jamba1.5:mini-ff0a`
llama3.1	8b	24G	`openllm serve llama3.1:8b`
llama3.2	1b	24G	`openllm serve llama3.2:1b`
llama3.3	70b	80Gx2	`openllm serve llama3.3:70b`
llama4	17b16e	80Gx8	`openllm serve llama4:17b16e`
mistral	8b-2410	24G	`openllm serve mistral:8b-2410`
mistral-large	123b-2407	80Gx4	`openllm serve mistral-large:123b-2407`
phi4	14b	80G	`openllm serve phi4:14b`
pixtral	12b-2409	80G	`openllm serve pixtral:12b-2409`
qwen2.5	7b	24G	`openllm serve qwen2.5:7b`
qwen2.5-coder	3b	24G	`openllm serve qwen2.5-coder:3b`
qwq	32b	80G	`openllm serve qwq:32b`

For the full model list, see the [OpenLLM models repository](https://github.com/bentoml/openllm-models). ## Start an LLM server To start an LLM server locally, use the `openllm serve` command and specify the model version. > [!NOTE] > OpenLLM does not store model weights. A Hugging Face token (HF_TOKEN) is required for gated models. > > 1. Create your Hugging Face token [here](https://huggingface.co/settings/tokens). > 2. Request access to the gated model, such as [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct). > 3. Set your token as an environment variable by running: > ```bash > export HF_TOKEN= > ``` ```bash openllm serve llama3.2:1b ``` The server will be accessible at [http://localhost:3000](http://localhost:3000/), providing OpenAI-compatible APIs for interaction. You can call the endpoints with different frameworks and tools that support OpenAI-compatible APIs. Typically, you may need to specify the following: - **The API host address**: By default, the LLM is hosted at [http://localhost:3000](http://localhost:3000/). - **The model name:** The name can be different depending on the tool you use. - **The API key**: The API key used for client authentication. This is optional. Here are some examples:

OpenAI Python client

```python from openai import OpenAI client = OpenAI(base_url='http://localhost:3000/v1', api_key='na') # Use the following func to get the available models # model_list = client.models.list() # print(model_list) chat_completion = client.chat.completions.create( model="meta-llama/Llama-3.2-1B-Instruct", messages=[ { "role": "user", "content": "Explain superconductors like I'm five years old" } ], stream=True, ) for chunk in chat_completion: print(chunk.choices[0].delta.content or "", end="") ```

LlamaIndex

```python from llama_index.llms.openai import OpenAI llm = OpenAI(api_bese="http://localhost:3000/v1", model="meta-llama/Llama-3.2-1B-Instruct", api_key="dummy") ... ```

## Chat UI OpenLLM provides a chat UI at the `/chat` endpoint for the launched LLM server at http://localhost:3000/chat. openllm_ui

## Chat with a model in the CLI To start a chat conversation in the CLI, use the `openllm run` command and specify the model version. ```bash openllm run llama3:8b ``` ## Model repository A model repository in OpenLLM represents a catalog of available LLMs that you can run. OpenLLM provides a default model repository that includes the latest open-source LLMs like Llama 3, Mistral, and Qwen2, hosted at [this GitHub repository](https://github.com/bentoml/openllm-models). To see all available models from the default and any added repository, use: ```bash openllm model list ``` To ensure your local list of models is synchronized with the latest updates from all connected repositories, run: ```bash openllm repo update ``` To review a model’s information, run: ```bash openllm model get llama3.2:1b ``` ### Add a model to the default model repository You can contribute to the default model repository by adding new models that others can use. This involves creating and submitting a Bento of the LLM. For more information, check out this [example pull request](https://github.com/bentoml/openllm-models/pull/1). ### Set up a custom repository You can add your own repository to OpenLLM with custom models. To do so, follow the format in the default OpenLLM model repository with a `bentos` directory to store custom LLMs. You need to [build your Bentos with BentoML](https://docs.bentoml.com/en/latest/guides/build-options.html) and submit them to your model repository. First, prepare your custom models in a `bentos` directory following the guidelines provided by [BentoML to build Bentos](https://docs.bentoml.com/en/latest/guides/build-options.html). Check out the [default model repository](https://github.com/bentoml/openllm-repo) for an example and read the [Developer Guide](https://github.com/bentoml/OpenLLM/blob/main/DEVELOPMENT.md) for details. Then, register your custom model repository with OpenLLM: ```bash openllm repo add

``` **Note**: Currently, OpenLLM only supports adding public repositories. ## Deploy to BentoCloud OpenLLM supports LLM cloud deployment via BentoML, the unified model serving framework, and BentoCloud, an AI inference platform for enterprise AI teams. BentoCloud provides fully-managed infrastructure optimized for LLM inference with autoscaling, model orchestration, observability, and many more, allowing you to run any AI model in the cloud. [Sign up for BentoCloud](https://www.bentoml.com/) for free and [log in](https://docs.bentoml.com/en/latest/bentocloud/how-tos/manage-access-token.html). Then, run `openllm deploy` to deploy a model to BentoCloud: ```bash openllm deploy llama3.2:1b --env HF_TOKEN ``` > [!NOTE] > If you are deploying a gated model, make sure to set HF_TOKEN in enviroment variables. Once the deployment is complete, you can run model inference on the BentoCloud console: bentocloud_ui

## Community OpenLLM is actively maintained by the BentoML team. Feel free to reach out and join us in our pursuit to make LLMs more accessible and easy to use 👉 [Join our Slack community!](https://l.bentoml.com/join-slack) ## Contributing As an open-source project, we welcome contributions of all kinds, such as new features, bug fixes, and documentation. Here are some of the ways to contribute: - Repost a bug by [creating a GitHub issue](https://github.com/bentoml/OpenLLM/issues/new/choose). - [Submit a pull request](https://github.com/bentoml/OpenLLM/compare) or help review other developers’ [pull requests](https://github.com/bentoml/OpenLLM/pulls). - Add an LLM to the OpenLLM default model repository so that other users can run your model. See the [pull request template](https://github.com/bentoml/openllm-models/pull/1). - Check out the [Developer Guide](https://github.com/bentoml/OpenLLM/blob/main/DEVELOPMENT.md) to learn more. ## Acknowledgements This project uses the following open-source projects: - [bentoml/bentoml](https://github.com/bentoml/bentoml) for production level model serving - [vllm-project/vllm](https://github.com/vllm-project/vllm) for production level LLM backend - [blrchen/chatgpt-lite](https://github.com/blrchen/chatgpt-lite) for a fancy Web Chat UI - [astral-sh/uv](https://github.com/astral-sh/uv) for blazing fast model requirements installing We are grateful to the developers and contributors of these projects for their hard work and dedication.