https://github.com/xorbitsai/inference
Swap GPT for any LLM by changing a single line of code. Xinference lets you run open-source, speech, and multimodal models on cloud, on-prem, or your laptop — all through one unified, production-ready inference API.
https://github.com/xorbitsai/inference
artificial-intelligence chatglm deployment flan-t5 gemma ggml glm4 inference llama llama3 llamacpp llm machine-learning mistral openai-api pytorch qwen vllm whisper wizardlm
Last synced: 5 days ago
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Swap GPT for any LLM by changing a single line of code. Xinference lets you run open-source, speech, and multimodal models on cloud, on-prem, or your laptop — all through one unified, production-ready inference API.
- Host: GitHub
- URL: https://github.com/xorbitsai/inference
- Owner: xorbitsai
- License: apache-2.0
- Created: 2023-06-14T07:05:04.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2026-04-21T21:07:27.000Z (9 days ago)
- Last Synced: 2026-04-21T22:03:25.554Z (9 days ago)
- Topics: artificial-intelligence, chatglm, deployment, flan-t5, gemma, ggml, glm4, inference, llama, llama3, llamacpp, llm, machine-learning, mistral, openai-api, pytorch, qwen, vllm, whisper, wizardlm
- Language: Python
- Homepage: https://inference.readthedocs.io
- Size: 69.8 MB
- Stars: 9,251
- Watchers: 61
- Forks: 821
- Open Issues: 51
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Xorbits Inference: Model Serving Made Easy 🤖
Xinference Enterprise ·
Self-hosting ·
Documentation
[](https://pypi.org/project/xinference/)
[](https://github.com/xorbitsai/inference/blob/main/LICENSE)
[](https://actions-badge.atrox.dev/xorbitsai/inference/goto?ref=main)
[](https://hub.docker.com/r/xprobe/xinference)
[](https://discord.gg/Xw9tszSkr5)
[](https://twitter.com/xorbitsio)
Xorbits Inference(Xinference) is a powerful and versatile library designed to serve language,
speech recognition, and multimodal models. With Xorbits Inference, you can effortlessly deploy
and serve your or state-of-the-art built-in models using just a single command. Whether you are a
researcher, developer, or data scientist, Xorbits Inference empowers you to unleash the full
potential of cutting-edge AI models.
## 🔥 Hot Topics
### Framework Enhancements
- Agent-native Serving: Xinference integrates with [Xagent](https://github.com/xorbitsai/xagent) to enable dynamic planning, tool use, and autonomous multi-step reasoning — moving beyond static pipelines.
- Auto batch: Multiple concurrent requests are automatically batched, significantly improving throughput: [#4197](https://github.com/xorbitsai/inference/pull/4197)
- [Xllamacpp](https://github.com/xorbitsai/xllamacpp): New llama.cpp Python binding, maintained by Xinference team, supports continuous batching and is more production-ready.: [#2997](https://github.com/xorbitsai/inference/pull/2997)
- Distributed inference: running models across workers: [#2877](https://github.com/xorbitsai/inference/pull/2877)
- VLLM enhancement: Shared KV cache across multiple replicas: [#2732](https://github.com/xorbitsai/inference/pull/2732)
### New Models
- Built-in support for [Gemma-4](https://deepmind.google/models/gemma/gemma-4/): [#4768](https://github.com/xorbitsai/inference/pull/4768)
- Built-in support for [Qwen3-TTS](https://github.com/QwenLM/Qwen3-TTS): [#4781](https://github.com/xorbitsai/inference/pull/4781)
- Built-in support for [Qwen-3.5](https://github.com/QwenLM/Qwen3.5): [#4639](https://github.com/xorbitsai/inference/pull/4639)
- Built-in support for [GLM-5](https://github.com/zai-org/GLM-5): [#4638](https://github.com/xorbitsai/inference/pull/4638)
- Built-in support for [MiniMax-M2.5](https://github.com/MiniMax-AI/MiniMax-M2.5): [#4630](https://github.com/xorbitsai/inference/pull/4630)
- Built-in support for [Kimi-K2.5](https://github.com/MoonshotAI/Kimi-K2.5): [#4631](https://github.com/xorbitsai/inference/pull/4631)
- Built-in support for [FLUX.2-Klein](https://bfl.ai/models/flux-2-klein): [#4596](https://github.com/xorbitsai/inference/pull/4596)
- Built-in support for [Qwen3-ASR](https://github.com/QwenLM/Qwen3-ASR): [#4581](https://github.com/xorbitsai/inference/pull/4581)
### Integrations
- [Xagent](https://github.com/xorbitsai/xagent): an enterprise agent platform for building and running AI agents with planning, memory, and tool use — not limited to rigid workflows.
- [Dify](https://docs.dify.ai/advanced/model-configuration/xinference): an LLMOps platform that enables developers (and even non-developers) to quickly build useful applications based on large language models, ensuring they are visual, operable, and improvable.
- [FastGPT](https://github.com/labring/FastGPT): a knowledge-based platform built on the LLM, offers out-of-the-box data processing and model invocation capabilities, allows for workflow orchestration through Flow visualization.
- [RAGFlow](https://github.com/infiniflow/ragflow): is an open-source RAG engine based on deep document understanding.
- [MaxKB](https://github.com/1Panel-dev/MaxKB): MaxKB = Max Knowledge Brain, it is a powerful and easy-to-use AI assistant that integrates Retrieval-Augmented Generation (RAG) pipelines, supports robust workflows, and provides advanced MCP tool-use capabilities.
## Key Features
🌟 **Model Serving Made Easy**: Simplify the process of serving large language, speech
recognition, and multimodal models. You can set up and deploy your models
for experimentation and production with a single command.
⚡️ **State-of-the-Art Models**: Experiment with cutting-edge built-in models using a single
command. Inference provides access to state-of-the-art open-source models!
🖥 **Heterogeneous Hardware Utilization**: Make the most of your hardware resources with
[ggml](https://github.com/ggerganov/ggml). Xorbits Inference intelligently utilizes heterogeneous
hardware, including GPUs and CPUs, to accelerate your model inference tasks.
⚙️ **Flexible API and Interfaces**: Offer multiple interfaces for interacting
with your models, supporting OpenAI compatible RESTful API (including Function Calling API), RPC, CLI
and WebUI for seamless model management and interaction.
🌐 **Distributed Deployment**: Excel in distributed deployment scenarios,
allowing the seamless distribution of model inference across multiple devices or machines.
🔌 **Built-in Integration with Third-Party Libraries**: Xorbits Inference seamlessly integrates
with popular third-party libraries including [LangChain](https://python.langchain.com/docs/integrations/providers/xinference), [LlamaIndex](https://gpt-index.readthedocs.io/en/stable/examples/llm/XinferenceLocalDeployment.html#i-run-pip-install-xinference-all-in-a-terminal-window), [Dify](https://docs.dify.ai/advanced/model-configuration/xinference), and [Chatbox](https://chatboxai.app/).
## Why Xinference
| Feature | Xinference | FastChat | OpenLLM | RayLLM |
|------------------------------------------------|------------|----------|---------|--------|
| OpenAI-Compatible RESTful API | ✅ | ✅ | ✅ | ✅ |
| vLLM Integrations | ✅ | ✅ | ✅ | ✅ |
| More Inference Engines (GGML, TensorRT) | ✅ | ❌ | ✅ | ✅ |
| More Platforms (CPU, Metal) | ✅ | ✅ | ❌ | ❌ |
| Multi-node Cluster Deployment | ✅ | ❌ | ❌ | ✅ |
| Image Models (Text-to-Image) | ✅ | ✅ | ❌ | ❌ |
| Text Embedding Models | ✅ | ❌ | ❌ | ❌ |
| Multimodal Models | ✅ | ❌ | ❌ | ❌ |
| Audio Models | ✅ | ❌ | ❌ | ❌ |
| More OpenAI Functionalities (Function Calling) | ✅ | ❌ | ❌ | ❌ |
## Using Xinference
- **Self-hosting Xinference Community Edition**
Quickly get Xinference running in your environment with this [starter guide](#getting-started).
Use our [documentation](https://inference.readthedocs.io/) for further references and more in-depth instructions.
- **Xinference for enterprise / organizations**
We provide additional enterprise-centric features. [send us an email](mailto:business@xprobe.io?subject=[GitHub]Business%20License%20Inquiry) to discuss enterprise needs.
## Staying Ahead
Star Xinference on GitHub and be instantly notified of new releases.
## Getting Started
* [Docs](https://inference.readthedocs.io/en/latest/index.html)
* [Built-in Models](https://inference.readthedocs.io/en/latest/models/builtin/index.html)
* [Custom Models](https://inference.readthedocs.io/en/latest/models/custom.html)
* [Deployment Docs](https://inference.readthedocs.io/en/latest/getting_started/using_xinference.html)
* [Examples and Tutorials](https://inference.readthedocs.io/en/latest/examples/index.html)
### Jupyter Notebook
The lightest way to experience Xinference is to try our [Jupyter Notebook on Google Colab](https://colab.research.google.com/github/xorbitsai/inference/blob/main/examples/Xinference_Quick_Start.ipynb).
### Docker
Nvidia GPU users can start Xinference server using [Xinference Docker Image](https://inference.readthedocs.io/en/latest/getting_started/using_docker_image.html). Prior to executing the installation command, ensure that both [Docker](https://docs.docker.com/get-docker/) and [CUDA](https://developer.nvidia.com/cuda-downloads) are set up on your system.
```bash
docker run --name xinference -d -p 9997:9997 -e XINFERENCE_HOME=/data -v :/data --gpus all xprobe/xinference:latest xinference-local -H 0.0.0.0
```
### K8s via helm
Ensure that you have GPU support in your Kubernetes cluster, then install as follows.
```
# add repo
helm repo add xinference https://xorbitsai.github.io/xinference-helm-charts
# update indexes and query xinference versions
helm repo update xinference
helm search repo xinference/xinference --devel --versions
# install xinference
helm install xinference xinference/xinference -n xinference --version 0.0.1-v
```
For more customized installation methods on K8s, please refer to the [documentation](https://inference.readthedocs.io/en/latest/getting_started/using_kubernetes.html).
### Quick Start
Install Xinference by using pip as follows. (For more options, see [Installation page](https://inference.readthedocs.io/en/latest/getting_started/installation.html).)
```bash
pip install "xinference[all]"
```
To start a local instance of Xinference, run the following command:
```bash
$ xinference-local
```
Once Xinference is running, there are multiple ways you can try it: via the web UI, via cURL,
via the command line, or via the Xinference’s python client. Check out our [docs]( https://inference.readthedocs.io/en/latest/getting_started/using_xinference.html#run-xinference-locally) for the guide.
## Getting involved
| Platform | Purpose |
|-------------------------------------------------------------------------------------------------|---------------------------------------------|
| [Github Issues](https://github.com/xorbitsai/inference/issues) | Reporting bugs and filing feature requests. |
| [Discord](https://discord.gg/Xw9tszSkr5) | Collaborating with other Xinference users. |
| [Twitter](https://twitter.com/xorbitsio) | Staying up-to-date on new features. |
## Citation
If this work is helpful, please kindly cite as:
```bibtex
@inproceedings{lu2024xinference,
title = "Xinference: Making Large Model Serving Easy",
author = "Lu, Weizheng and Xiong, Lingfeng and Zhang, Feng and Qin, Xuye and Chen, Yueguo",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.30",
pages = "291--300",
}
```
## Contributors
## Star History
[](https://star-history.com/#xorbitsai/inference&Date)