The need for computing power and bandwidth has grown exponentially due to rapid advances in Large Language Models (LLMs) and deep learning. The complexity and size of these models, which require massive amounts of data and processing power to be properly trained, are the main drivers of this surge in demand. However, building high-performance computing systems is much more expensive due to the high cost of faster processor cores and sophisticated interconnects. This presents a significant obstacle for companies trying to increase their AI capabilities while keeping costs under control.

To address these limitations, a team of researchers at DeepSeek-AI developed the Fire-Flyer AI HPC architecture, a comprehensive framework that synergistically brings together hardware and software design. This methodology focuses on cost efficiency and energy saving in addition to performance optimization. The team implemented the Fire-Flyer 2, a state-of-the-art system with 10,000 PCIe A100 GPUs specifically designed for DL ​​training activities.

One of the most notable achievements of the Fire-Flyer 2 is its ability to deliver a level of performance comparable to the industry-leading NVIDIA DGX-A100. All of this was achieved with a 50% reduction in cost and a 40% reduction in power consumption. The savings are due to careful engineering and conscious design decisions that optimize the system’s hardware and software components.

HFReduce, a specially designed method to accelerate all-reduce communication, a crucial process in distributed training, is one of the architecture’s key innovations. To maintain high throughput on large-scale training workloads, the efficiency of data exchange between GPUs must be dramatically improved, which is greatly improved by HFReduce. The team has also taken a number of other measures to ensure that the Computation-Storage Integrated Network is not overloaded, which will improve the overall reliability and performance of the system.

Tools like HaiScale, 3FS, and the HAI Platform are part of a powerful software stack that supports the Fire-Flyer AI HPC architecture. Together, these pieces improve scalability by sharing compute and communication tasks, allowing the system to effectively handle workloads that grow larger and more complicated over time.

In summary, the Fire-Flyer AI HPC architecture represents a major step forward in the development of affordable, high-performance computing systems for artificial intelligence. With a strong focus on cost and energy efficiency, the team has developed a system that meets the increasing demands of DL and LLMs by combining state-of-the-art hardware and software solutions.

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Tanya Malhotra is a final year student at the University of Petroleum & Energy Studies, Dehradun, pursuing her Bachelor of Tech in Computer Science Engineering with specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking skills and a keen interest in learning new skills, leading groups and getting work done in an organized manner.