LitServe is a Python framework for building high-performance AI inference pipelines, targeting developers who need to deploy models, agents, or RAG systems without complex MLOps or YAML configurations. It offers a significant speedup over standard FastAPI for AI workloads, enabling easier integration of multiple models, vector databases, and streaming responses with built-in GPU autoscaling and batching.

How It Works

LitServe leverages FastAPI as its foundation but introduces specialized multi-worker handling optimized for AI inference, claiming a 2x performance improvement. Users define inference pipelines using a LitAPI class, specifying model loading and execution logic within setup, decode_request, predict, and encode_response methods. This approach allows for complex, multi-stage processing and seamless integration of various AI components, including external libraries like vLLM.

Quick Start & Requirements

• Install via pip: pip install litserve
• Run locally: lightning serve server.py --local
• Deploy to Lightning AI: lightning serve server.py
• Requires Python. GPU acceleration is supported and recommended for performance.
• Examples and documentation are available: Quick start, Examples, Docs

Highlighted Details

• Claims 2x+ performance improvement over plain FastAPI for AI workloads.
• Supports complex inference pipelines with multiple models, batching, and streaming.
• Offers GPU autoscaling and integrates with popular LLM serving libraries like vLLM.
• Provides both self-hosting and a managed, serverless deployment option via Lightning AI.
• Features OpenAI-compatible API endpoints.

Maintenance & Community

LitServe is an active community project with a Discord server for support and contributions. The project is associated with Lightning AI.

Licensing & Compatibility

Licensed under Apache 2.0, which is permissive and generally compatible with commercial and closed-source applications.

Limitations & Caveats

While LitServe aims for ease of use, achieving maximum performance, especially for LLMs, may require specific optimizations like KV-caching, which are not automatically handled by default. The managed hosting features are tied to the Lightning AI platform.