Detailed Introduction
Chitu is a production-oriented inference engine focused on delivering high-performance, low-latency inference for large language models (LLMs). It supports deployments ranging from CPU-only and single-GPU setups to large-scale cluster environments, and provides compatibility with multiple hardware vendors to accommodate enterprise rollout.
Main Features
- Multi-hardware support: optimized implementations for NVIDIA and various domestic accelerators.
- Scalable deployment: supports single-node, heterogeneous CPU/GPU setups and distributed clusters.
- Production stability: engineering efforts for long-term stable operation under concurrent loads.
- Tooling and docs: official images, developer guides and performance benchmarks for fast validation and adoption.
Use Cases
Suitable for on-premise or edge LLM inference needs such as enterprise Q&A, realtime online inference services, batched model serving, and scenarios requiring domestic accelerator support or mixed-hardware optimization.
Technical Features
Chitu combines high-performance operator implementations, quantization and mixed-precision support (e.g., FP4/FP8/BF16), streaming and batch optimizations, and provides local images and benchmark documentation to facilitate engineering adoption. The project emphasizes extensibility and compatibility with mainstream LLMs via adapters and plugins.