Lightning-AI/LitServe
LitServe
LitServe is a Python AI inference server framework and LLM serving framework designed for high-concurrency inference. It functions as a distributed AI model server and dynamic batching inference engine, providing the tools to build and host custom servers that run AI models.
The framework distinguishes itself through a dynamic-batching request queue that groups individual inference requests into single tensors to maximize GPU throughput. It supports distributed GPU scaling, allowing model workloads to be spread across multiple hardware accelerators to balance compute loads and increase total capacity.
The system provides a high-level wrapper interface that decouples request preprocessing and postprocessing from the core model execution logic. It also includes capabilities for real-time model streaming to deliver outputs incrementally and uses an asynchronous event loop to handle concurrent network requests.
Features
- Dynamic Inference Batching - Implements a dynamic-batching request queue to maximize GPU throughput by grouping individual requests.
- AI Model Orchestration - Allows defining specific logic for how data is routed and processed through AI models.
- AI Workflow Orchestration - Provides capabilities to define custom orchestration logic for routing and processing data through AI models.
- Dynamic Batching Engines - Groups individual real-time requests into batches to maximize GPU throughput.
- Distributed Inference Scaling - Distributes model workloads across multiple hardware nodes to increase throughput and balance resources.
- Inference Servers - Offers a dedicated server framework for hosting and executing machine learning models.
- Inference Server Frameworks - Provides a high-level wrapper interface that encapsulates server boilerplate for rapid deployment of custom inference logic.
- Distributed Model Servers - Distributes model workloads across multiple accelerators to handle high-concurrency inference requests.
- Incremental Inference Streaming - Enables the incremental delivery of model outputs to clients to reduce perceived latency.
- Serving Frameworks - Provides a high-throughput deployment and serving framework specifically for large language models.
- Model Response Streaming - Ships a generator that sends model outputs incrementally to the client.
- Inference Servers - Functions as a network service that hosts AI models and provides predictions via a Python API.
- Real-Time Text Streaming - Sends AI generated responses to the user incrementally as they are produced.
- Inference Batching - Groups multiple incoming AI requests into single batches to maximize GPU hardware utilization.
- Multi-GPU Workload Distribution - Spreads inference tasks across multiple hardware accelerators to balance compute loads.
- Request Batching - Combines multiple individual requests into a single execution to optimize hardware utilization.
- Logic Decoupling - Decouples request preprocessing and postprocessing from the core model execution logic.
- Asyncio Coroutine Execution - Uses an asynchronous event loop to handle concurrent network requests without blocking execution.
- Inference and Serving - Fast serving engine with support for batching and autoscaling.
- Local Development and Serving - Deploy high-speed GPU inference with autoscaling capabilities.
- Model Serving - Flexible and easy-to-use serving engine for AI models.
- Model Serving & Deployment - Provides a flexible serving engine built on FastAPI.
- Model Serving Frameworks - Lightweight, high-performance serving engine for AI models.
- PyTorch Ecosystem - Model serving framework for PyTorch.
Star history
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