Beijing, [Date] – In a significant step towards democratizing access to powerful AI technology, Tsinghua University’s Institute for High-Performance Computing and Qingcheng Zhiji have jointly open-sourced Chitu (赤兔), a high-performance inference engine designed to tackle the challenges of cost and efficiency associated with deploying large language models (LLMs).
The release of Chitu comes at a crucial time, as the demand for LLMs continues to surge across various industries. However, the computational resources required for inference, particularly at scale, often present a significant barrier to entry for many organizations. Chitu aims to address this bottleneck by offering a highly optimized and versatile solution.
What is Chitu?
Chitu, named after the legendary red hare known for its speed and endurance, is engineered to deliver exceptional performance in LLM inference. Its key strength lies in its ability to adapt to a wide range of hardware configurations, breaking the dependence on specific architectures like NVIDIA’s Hopper.
Key Features and Benefits:
- Diverse Hardware Adaptation: Chitu supports a wide range of NVIDIA GPUs, from the latest flagship models to older generations, ensuring compatibility with existing infrastructure. Crucially, it also provides optimized support for domestically produced chips, fostering technological independence.
- Scalability Across Scenarios: From CPU-only deployments to single-GPU setups and large-scale clusters, Chitu offers scalable solutions tailored to different needs and deployment environments. This flexibility allows organizations to optimize resource allocation and cost-effectively deploy LLMs.
- Low-Latency Optimization: For latency-sensitive applications such as financial risk control, Chitu prioritizes speed, minimizing response times and enabling real-time decision-making.
- High-Throughput Optimization: In high-concurrency scenarios like intelligent customer service, Chitu maximizes the number of requests processed per unit of time, ensuring efficient handling of large volumes of interactions.
- Small Memory Footprint Optimization: By reducing the memory footprint per card, Chitu enables businesses to achieve higher inference performance with fewer hardware resources, leading to significant cost savings.
- Production-Ready Stability: Chitu is designed for long-term, stable operation in real-world production environments, ensuring reliability and minimizing downtime.
Performance Benchmarks:
Early results are promising. When deploying DeepSeek-R1-671B on an A800 cluster, Chitu demonstrated a 50% reduction in GPU usage and a 3.15x increase in inference speed compared to some foreign open-source frameworks. These performance gains highlight Chitu’s potential to significantly reduce the cost and improve the efficiency of LLM deployments.
Implications and Future Directions:
The open-source release of Chitu is 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 a wider range of organizations to leverage the power of LLMs. This move is expected to accelerate innovation and adoption of AI across various sectors.
The developers plan to continue improving Chitu, focusing on expanding hardware support, optimizing performance, and adding new features. The open-source nature of the project encourages community contributions and collaboration, further accelerating its development and ensuring its long-term viability.
Chitu represents a significant step forward in making large language models more accessible and practical for a wider audience. Its ability to adapt to diverse hardware configurations, optimize for different performance metrics, and operate reliably in production environments positions it as a valuable tool for organizations looking to harness the power of AI.
References:
- [Original Source Article Link (if available)]
- [Tsinghua University Institute for High-Performance Computing Website (if available)]
- [Qingcheng Zhiji Website (if available)]
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