Chitu is a high-performance inference framework for large language models that prioritizes efficiency, flexibility, and availability across diverse hardware and deployment scales.
The framework addresses the challenge of deploying large language models efficiently in production environments by providing optimized inference across multiple compute platforms and deployment scenarios. It supports a range of hardware from NVIDIA GPUs across different generations to domestic Chinese processors, and scales from CPU-only deployments through single-GPU setups to large cluster configurations. The tool includes efficient operator implementations for quantization conversions, such as FP4 to FP8 or BF16, and supports heterogeneous CPU-GPU inference to enable single-card deployment of very large models.
Teams should adopt this framework if they need production-grade LLM inference with broad hardware compatibility and the ability to scale from experimental deployments to enterprise-scale operations. It suits organizations working with models like DeepSeek, Qwen, GLM, and Kimi that require stable, concurrent traffic handling. The project acknowledges that while it welcomes community contributions and issue reports, team capacity constraints may limit response times for user problems, and professional technical support is available through direct contact.
Development activity shows consistent release cadence with incremental feature additions and hardware support expansions. The project maintains a public performance benchmarking document and actively solicits community-contributed test data. The team has expanded platform support progressively, adding compatibility for emerging domestic GPU architectures alongside continued optimization for NVIDIA hardware. Documentation includes development guides and contribution guidelines, indicating structured project governance.
