---
title: "OpenLLM"
type: "tool"
slug: "bentoml-openllm"
canonical_url: "https://www.graphcanon.com/tools/bentoml-openllm"
github_url: "https://github.com/bentoml/OpenLLM"
homepage_url: "https://bentoml.com"
stars: 12388
forks: 822
primary_language: "Python"
license: "Apache-2.0"
categories: ["llm-frameworks", "inference-serving"]
tags: ["llama", "mistral", "fine-tuning", "bentoml", "model-inference", "open-source-llm"]
updated_at: "2026-07-07T18:32:00.502339+00:00"
---

# OpenLLM

> Self-hosting LLMs made easy

OpenLLM is a tool for running open-source and custom language models as OpenAI-compatible APIs, simplifying cloud deployment.

## Facts

- Repository: https://github.com/bentoml/OpenLLM
- Homepage: https://bentoml.com
- Stars: 12,388 · Forks: 822 · Open issues: 17 · Watchers: 82
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2026-06-29T16:55:17+00:00

## Categories

- [LLM Frameworks](/categories/llm-frameworks.md)
- [Inference & Serving](/categories/inference-serving.md)

## Tags

llama, mistral, fine-tuning, bentoml, model-inference, open-source-llm

## Related tools

- [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,659)
- [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,019)
- [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347)
- [open-webui](/tools/open-webui-open-webui.md) - User-friendly AI Interface (Supports Ollama, OpenAI API, ...) (★ 144,575)
- [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. (★ 116,702)
- [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch (★ 98,711)
- [TradingAgents](/tools/tauricresearch-tradingagents.md) - TradingAgents: Multi-Agents LLM Financial Trading Framework (★ 91,610)
- [caveman](/tools/juliusbrussee-caveman.md) - Cuts 65% of tokens in AI coding agent responses. (★ 86,150)

## README (excerpt)

```text
<div align="center">

<h1>🦾 OpenLLM: Self-Hosting LLMs Made Easy</h1>







</div>

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
```



## 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.

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

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=<your 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
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/bentoml-openllm`](/api/graphcanon/tools/bentoml-openllm)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_