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
- Xinference 4.0.0 is available with multi-engine Xavier, heterogeneous P/D, and runtime model reconfiguration: Release Notes
- Agent-native Serving: Xinference integrates with 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
- Xllamacpp: New llama.cpp Python binding, maintained by Xinference team, supports continuous batching and is more production-ready.: #2997
- Distributed inference: running models across workers: #2877
- VLLM enhancement: Shared KV cache across multiple replicas: #2732
New Models
Integrations
- Xagent: an enterprise agent platform for building and running AI agents with planning, memory, and tool use — not limited to rigid workflows.
- Dify: 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: 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: is an open-source RAG engine based on deep document understanding.
- 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. 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, LlamaIndex, Dify, and Chatbox.
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.
Use our documentation for further references and more in-depth instructions.
-
Xinference for enterprise / organizations
Xinference Enterprise is the commercial edition for teams running models in production. Deploy it in your own cloud or data centre, or choose a managed dedicated deployment with Xinference Cloud. The hosted Model API gives you access to supported models through an OpenAI-compatible API without managing inference infrastructure. Learn more at xinference.co.
For enterprise enquiries, email us.
Staying Ahead
Star Xinference on GitHub and be instantly notified of new releases.
Getting Started
Quick Start
pip
pip install xinference
xinference-local
One-command installer
Linux/macOS:
curl -fsSL https://raw.githubusercontent.com/xorbitsai/inference/main/scripts/install.sh | sh
Windows PowerShell:
irm https://raw.githubusercontent.com/xorbitsai/inference/main/scripts/install.ps1 | iex
Installation guide · Usage guide
Docker
Nvidia GPU users can start Xinference server using Xinference Docker Image. Prior to executing the installation command, ensure that both Docker and CUDA are set up on your system.
docker run --name xinference -d -p 9997:9997 -e XINFERENCE_HOME=/data -v </on/your/host>:/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<xinference_release_version>
For more customized installation methods on K8s, please refer to the documentation.
Getting involved
| Platform |
Purpose |
| Github Issues |
Reporting bugs and filing feature requests. |
| Discord |
Collaborating with other Xinference users. |
| Telegram |
Chatting with other Xinference users. |
| Twitter |
Staying up-to-date on new features. |
Citation
If this work is helpful, please kindly cite as:
@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
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