Overview
LMDeploy provides end-to-end model compression, quantization and deployment capabilities, including high-performance engines (TurboMind), continuous batching and distribution services for latency-sensitive production workloads.
Key features
- High-performance inference engines (TurboMind and optimized PyTorch backends).
- Quantization and KV-cache optimization to reduce memory footprint and latency.
- Deployment and distribution for offline batch and online multi-host serving.
Use cases
- Convert research models into production inference services with minimal effort.
- Serve high-concurrency, low-latency applications such as chat APIs.
Technical notes
- Supports multiple backends and model formats; see project docs for compatible models and installation.
- Includes benchmarking and visualization tooling for performance evaluation.