What are AI compute clusters and how to choose yours?

The rapid growth of AI, particularly in training and running large foundational models, has necessitated the need for powerful computational infrastructures, with GPU clusters being a key solution. These clusters, often comprising dozens to thousands of GPUs, are essential for handling the intense computational demands of Generative AI and large language models (LLMs), which cannot be managed by a single GPU. GPU clusters facilitate parallel computing, breaking down large tasks into smaller operations assigned to interconnected GPUs, thereby accelerating processes such as model training, fine-tuning, and inferencing. The orchestration of these clusters involves both hardware—comprising head and worker nodes equipped with GPUs, CPUs, RAM, and NICs—and software, with tools like Kubernetes and Slurm managing resource allocation and task scheduling. Networking and storage within GPU clusters are crucial for maintaining high performance, with fast data transfer and retrieval speeds required for effective training and checkpointing. Selecting the right GPU cluster involves considering factors such as hardware quality, networking, storage capabilities, costs, and provider offerings, with an emphasis on reliability, power efficiency, and sustainability.

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