Beijing – In a significant move towards democratizing access to powerful AI technology, Tsinghua University’s Institute of High-Performance Computing, in collaboration with Qingcheng Zhiji, has announced the open-source release of Chitu, a high-performance inference engine designed specifically for large language models (LLMs). This development addresses a critical bottleneck in the deployment of LLMs: the high cost and inefficiency associated with the inference stage.
LLMs, with their immense computational demands, often require specialized hardware and significant resources for effective deployment. Chitu aims to alleviate these challenges by offering a versatile and optimized solution that can run efficiently on a wide range of hardware, from CPUs to large-scale GPU clusters.
What is Chitu?
Chitu (赤兔), named after the legendary steed in Chinese folklore, is engineered to provide a robust and adaptable inference engine for LLMs. Its core mission is to break down the barriers to entry for organizations seeking to leverage the power of LLMs without incurring exorbitant infrastructure costs.
Key Features and Benefits:
- Diverse Hardware Adaptability: Chitu supports a broad spectrum of NVIDIA GPUs, from the latest flagship models to older generations. Crucially, it also provides optimized support for domestically produced Chinese chips, reducing reliance on specific architectures like NVIDIA’s Hopper. This is a significant step towards fostering technological independence and supporting the growth of China’s domestic semiconductor industry.
- Scalability for All Scenarios: Whether deploying on a single CPU, a single GPU, or a massive cluster, Chitu offers scalable solutions to meet varying inference demands. This flexibility makes it suitable for a wide range of applications, from small-scale research projects to large-scale commercial deployments.
- Low-Latency Optimization: For latency-sensitive applications such as financial risk control, Chitu prioritizes minimizing response times, ensuring rapid and efficient decision-making.
- High-Throughput Optimization: In high-concurrency scenarios, such as intelligent customer service, Chitu maximizes the number of requests processed per unit of time, enhancing overall system efficiency and responsiveness.
- Reduced Memory Footprint: Chitu is designed to minimize the memory footprint on individual GPUs, enabling organizations to achieve higher inference performance with fewer hardware resources. This is particularly beneficial for companies with limited budgets or those seeking to optimize their existing infrastructure.
- Production-Ready Stability: Chitu is designed for long-term stability and reliability in real-world production environments, ensuring consistent performance and minimizing downtime.
Performance Benchmarks:
Early performance benchmarks are promising. When deploying the DeepSeek-R1-671B model on an A800 GPU cluster, Chitu demonstrated a 50% reduction in GPU usage and a 3.15x increase in inference speed compared to some other open-source frameworks. These figures highlight the potential of Chitu to significantly improve the efficiency and cost-effectiveness of LLM deployments.
Implications and Future Directions:
The open-source release of Chitu represents a significant contribution to the AI community. By providing a high-performance, adaptable, and cost-effective inference engine, Tsinghua University and Qingcheng Zhiji are empowering researchers, developers, and organizations to unlock the full potential of large language models. This initiative is likely to accelerate the adoption of LLMs across various industries and contribute to the advancement of AI technology as a whole.
Further development and community contributions are expected to enhance Chitu’s capabilities and expand its support for even more hardware platforms and LLM architectures. As the AI landscape continues to evolve, Chitu is poised to play a crucial role in shaping the future of LLM deployment and accessibility.
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