[Blog] <Thorough Explanation> Just Increasing GPU Servers Won't Make It Faster?
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Key Points to Consider When Building an HPC Cluster for AI and LLMs In the development of generative AI and large language models (LLMs), there is an increasing number of cases where a single GPU workstation or GPU server cannot provide the necessary processing power due to the growth in model size and the amount of training data. As a solution, an HPC cluster that connects multiple GPU servers becomes an option. However, simply adding multiple GPU servers does not guarantee an increase in processing performance proportional to the number of units. If there are issues with the inter-node network, storage, software environment, or resource management, the GPUs may not be able to fully utilize their inherent performance. This article will explain the common bottlenecks that arise in HPC clusters for AI and LLM development, along with key points to check when considering the configuration, incorporating insights from actual consultations.
