Jan Peter Alexander

There is nothing certain than the uncertainty itself. Capture, learn and mitigate. Welcome!

Xinference is an open-source library developed by Xorbits that provides a unified platform for serving various AI models, including language models, speech recognition systems, and multimodal models. It simplifies the deployment and management of AI inference workloads with support for multiple backends and optimization techniques.

Key Features

Xinference stands out with its comprehensive model support and flexible deployment options:

  • Multi-Model Support: Handles language models (LLMs), speech-to-text, text-to-image, image-to-video, and multimodal models
  • Multiple Backends: Compatible with transformers, vLLM, SGLang, llama.cpp, MLX, and other inference engines
  • Model Formats: Supports PyTorch, GGUF, GPTQ, AWQ, and FP8 formats with various quantization options
  • Deployment Flexibility: Run locally, in distributed clusters, or on Kubernetes via Helm charts
  • Authentication System: Built-in auth and authorization for secure deployments
  • Continuous Batching: Optimized for high-throughput inference with continuous batching support

Quick Start

Getting started with Xinference is straightforward:

# Install Xinference with all dependencies
pip install "xinference[all]"

# Start local server
xinference-local

# Launch a model (example: Qwen2.5-Instruct 7B)
xinference launch --model-engine transformers \
  --model-name qwen2.5-instruct \
  --size-in-billions 7 \
  --model-format pytorch \
  --quantization none

Supported Models

Xinference supports a wide range of popular models including:

  • Language Models: Qwen, Llama, ChatGLM, Phi, and many others
  • Multimodal Models: Qwen2-VL, LLaVA for vision-language tasks
  • Audio Models: Whisper, SenseVoice for speech recognition
  • Image Generation: Stable Diffusion models
  • Video Models: Wan2.1 for image-to-video generation

Use Cases

Xinference is ideal for:

  • Research and development environments
  • Production AI service deployment
  • Edge computing with quantized models
  • Multi-tenant AI platforms
  • Educational and experimental setups

The platform’s unified API and extensive model support make it a powerful tool for organizations looking to deploy diverse AI capabilities efficiently. Whether you’re running a single model locally or managing a cluster of GPU workers, Xinference provides the flexibility and performance needed for modern AI inference workloads.

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