Recently, DeepSeek's affiliated company —— Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., has publicly disclosed a patent titled "A Deployment Method and System for a Large Language Model." The release of this patent marks another significant advancement by the company in the field of artificial intelligence, especially in the deployment of large language models.
Image source note: The image is AI-generated, and the image licensing service provider is Midjourney
According to the patent summary, this invention mainly involves core technologies in artificial intelligence. Its innovation lies in deploying two key stages of a large language model —— the pre-filling stage and the decoding stage —— on machines with high-performance computing capabilities and large memory. This distributed deployment method can effectively balance workload tasks and maximize hardware resource utilization. By reducing idle computing capacity, this method not only reduces overall latency but also significantly improves system throughput.
In the current development of AI technology, system scalability and fault tolerance have become particularly important. DeepSeek's patent achieves this by optimizing resource allocation, enhancing the system's adaptability to different workloads. This innovative deployment method suggests that future AI models will be more efficient and intelligent, and are expected to provide better support for various application scenarios.
Notably, DeepSeek-V3, one of the company's core products, features a powerful Mixture-of-Experts (MoE) language model with 671B parameters, and each token activates 37B parameters. This technological advancement will undoubtedly promote the popularization and application of AI technology, supporting the digital transformation of various industries.
Key points:
🌟 DeepSeek releases a new patent, innovating the method of deploying large language models, improving system performance.
🚀 Distributed deployment maximizes hardware resource utilization, reducing overall latency.
📈 Enhances system scalability and fault tolerance, supporting the development of future AI technology.