inference
Xinference is an open-source Python library for deploying and serving large language models, speech recognition, and multimodal AI models through a unified API. It supports multiple model backends, hardware types, and deployment scenarios—from laptops to distributed cloud infrastructure—with OpenAI-compatible REST endpoints.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | xorbitsai/inference |
| Owner | xorbitsai |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 9.4k |
| Forks | 845 |
| Open issues | 47 |
| Latest release | v2.12.0 (2026-07-04) |
| Last updated | 2026-07-07 |
| Source | https://github.com/xorbitsai/inference |
What inference is
Built in Python, Xinference abstracts LLM serving across inference engines (vLLM, llama.cpp, PyTorch), hardware accelerators (GPU/CPU), and deployment topologies (single-node, distributed). It provides OpenAI-compatible REST API, RPC, CLI, and WebUI, with built-in support for dozens of open-source models (LLaMA, Mistral, Qwen, ChatGLM, Gemma, Flan-T5, Whisper) and auto-batching for throughput optimization.
Get the inference source
Clone the repository and explore it locally.
git clone https://github.com/xorbitsai/inference.gitcd inference# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Python-only deployment: plan for Python runtime, dependency isolation, and package management (pip, Docker containers).
- Model storage and caching: ensure sufficient disk/GPU VRAM for downloaded models; implement cache invalidation strategy if running multiple model versions.
- Hardware heterogeneity: test inference engine support (vLLM, llama.cpp) and performance tuning on target GPU/CPU combinations; quantization and batching settings have significant throughput impact.
- API compatibility: while OpenAI-compatible, verify function calling and model-specific features work with downstream clients (LangChain, Dify, etc.).
- Distributed setup: if scaling across workers, review documentation for orchestration requirements (networking, model replication, KV cache sharing).
When to avoid it — and what to weigh
- Proprietary Model Dependency — If your workflow requires exclusive access to closed-source models (GPT-4, Claude, Gemini), Xinference's strength in open-source model serving provides limited value.
- Minimal DevOps Capacity — Self-hosting requires infrastructure setup, dependency management, and ongoing operational overhead. Teams without DevOps support or preferring fully managed services should consider cloud providers.
- Sub-Millisecond Latency Requirements — Xinference adds abstraction layers over inference engines. Applications with strict ultra-low-latency SLAs may benefit from direct engine configuration or specialized solutions.
- Enterprise Compliance & Audit Mandates — No clear evidence in provided data of SOC 2, HIPAA, or FedRAMP compliance certifications. Regulated workloads require explicit security audit before adoption.
License & commercial use
Apache License 2.0 (Apache-2.0). A permissive, OSI-approved license allowing use in proprietary and commercial applications, with source distribution, modification, and sublicensing rights. Requires retention of license and copyright notices; no patent indemnification.
Apache 2.0 explicitly permits commercial use, including in proprietary software and closed-source deployments. No license restrictions on monetizing applications built with Xinference. However, review your deployment of underlying models (many open-source models have their own licenses); ensure compliance with both Xinference and any model-specific licensing terms. An enterprise offering (Xinference Enterprise) is mentioned; clarify whether support, SLAs, or additional features require separate commercial agreement.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Not assessed. Provided data does not detail security hardening, vulnerability disclosure policy, input validation, model injection safeguards, or access control mechanisms. Self-hosted models must be sourced responsibly (HuggingFace Hub etc.); model weights and inference data transit require encryption at rest and in motion. No evidence of security audit or certification. Operators should conduct threat model review, dependency scanning, and network isolation before production deployment with sensitive data.
Alternatives to consider
vLLM (bare engine)
Purpose-built high-performance LLM serving engine; lower-level control but requires manual orchestration and does not provide unified model abstraction.
FastChat
Lightweight open-source LLM serving tool with similar multi-model support; less feature-rich (no distributed inference comparison clear in data) but simpler deployment for small teams.
Ray LLM / RayServe
General-purpose distributed inference platform with strong Kubernetes integration; broader than LLM-only (supports arbitrary workloads) but steeper learning curve and higher operational overhead.
Build on inference with DEV.co software developers
Xinference gives you production-ready model serving with OpenAI-compatible APIs and multi-model support. Evaluate installation, distributed setup, and integration with your stack—contact us for deployment architecture review.
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inference FAQ
Can I run Xinference on a laptop or do I need GPU resources?
Does Xinference work with closed-source models like GPT-4?
What is the difference between Xinference open-source and Xinference Enterprise?
How do I migrate from OpenAI API to Xinference?
Software developers & web developers for hire
Adopting inference is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate ai frameworks software in production.
Ready to Serve Your LLMs Without Cloud APIs?
Xinference gives you production-ready model serving with OpenAI-compatible APIs and multi-model support. Evaluate installation, distributed setup, and integration with your stack—contact us for deployment architecture review.