Demand for processing power and bandwidth has increased exponentially due to rapid advances in large language models (LLMs) and deep learning. The complexity and size of these models, which require huge amounts of data and computing power to properly train, are the main causes of this increase in demand. However, building high-performance computing systems is much more expensive due to the high cost of faster processing cores and sophisticated interconnects. This poses a major hurdle for companies trying to scale their ai capabilities while controlling costs.
To address these limitations, a team of researchers at DeepSeek-ai has developed the Fire-Flyer ai-HPC architecture, a comprehensive framework that synergistically fuses hardware and software design. This approach prioritizes cost-effectiveness and energy conservation, in addition to performance optimization. The team has deployed 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 Fire-Flyer 2 is its ability to deliver performance levels comparable to the industry-leading NVIDIA DGX-A100. All of this has been achieved with a 50% reduction in cost and 40% reduction in power consumption. The savings can be attributed to careful engineering and deliberate design decisions that optimize the system’s hardware and software components.
HFReduce, a purpose-built method for accelerating all-reduce communication, a crucial process in distributed training, is one of the architecture’s key innovations. Maintaining high performance on large-scale training workloads requires dramatically improving the efficiency of data exchange between GPUs, something that HFReduce greatly improves. The team has also taken other steps to ensure that the integrated compute and storage network does not experience any congestion, which will increase the overall system reliability and performance.
Tools like HaiScale, 3FS, and HAI-Platform are part of a robust software stack that supports Fire-Flyer’s ai-HPC architecture. Together, these parts improve scalability by sharing computing and communication tasks, allowing the system to effectively manage workloads that grow larger and more complicated over time.
In conclusion, Fire-Flyer’s ai-HPC architecture is a major advancement in the development of affordable, high-performance computing systems for ai. With a strong focus on energy and cost efficiency, the team has developed a system that meets the growing requirements of distance learning and e-learning by combining cutting-edge hardware and software solutions.
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Tanya Malhotra is a final year student of the University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Engineering with specialization in artificial intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking skills, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
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