Introduction
Checkpoint Engine is a lightweight middleware for in-place model weight updates across LLM inference engines. It provides efficient broadcast and P2P update strategies to minimize synchronization time in large distributed deployments.
Key features
- Efficient broadcast and P2P weight update implementations.
- Pipelined data transfer to reduce copies and latency.
- Compatibility with vLLM and easy installation via PyPI.
Use cases
- Fast weight synchronization for large-scale inference clusters.
- Weight migration when instances are added or removed.
- Online weight updates in systems that require low-latency model refresh (e.g., RLHF workflows).
Technical highlights
- Three-stage transfer (H2D -> broadcast -> reload) for optimized memory and bandwidth use.
- Supports RDMA and other high-performance interconnects for P2P updates.
- Python-based implementation with examples and packaging on PyPI.