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LitServe is a minimal Python framework designed for building custom AI inference servers with full control over how models are executed and served. It allows developers to define their own inference logic, making it suitable for complex systems such as multi-model pipelines, agents, and retrieval-augmented generation workflows. Unlike traditional serving tools that enforce rigid abstractions, LitServe focuses on flexibility by letting users control request handling, batching strategies, and output processing directly in Python. LitServe is built on top of FastAPI and extends it with AI-specific optimizations such as efficient multi-worker execution, which can significantly improve throughput. It includes built-in capabilities for batching, streaming responses, and automatic scaling across CPUs and GPUs, enabling high-performance deployments.
Features
- Customizable inference logic with full control over request handling
- Built-in batching and streaming for efficient request processing
- GPU and multi-worker autoscaling for high-throughput workloads
- Supports multi-model pipelines, agents, and multimodal systems
- FastAPI-based architecture with performance optimizations
- Flexible deployment options including local and scalable environments
Project Samples
