GPU cluster in cooling data center

GPU cluster in a cooling data center: critical infrastructure for large-scale AI model training.

About this subject

GPU clusters form the backbone of artificial intelligence model training, especially large language models (LLMs) and computer vision systems. In modern data centers, tens or hundreds of GPUs are interconnected via high-speed networks like NVLink or InfiniBand to process petabytes of data simultaneously. Cooling is a critical challenge: each GPU can dissipate hundreds of watts, requiring liquid cooling or precision air conditioning to prevent overheating and ensure stable performance.

Brazil hosts hyperscale data centers in São Paulo, Campinas, and Rio de Janeiro, operated by companies such as Google, Microsoft, and AWS, using GPU clusters for cloud services and research. Demand for computational capacity grows exponentially with the adoption of generative AI, driving infrastructure investments. In 2024, Brazil's data center market moved over R$ 10 billion, with an expected annual growth of 15%.

Notably, the first general-purpose GPU cluster was the Nvidia DGX-1, launched in 2016, integrating eight Tesla P100 GPUs. Today, systems like the Nvidia DGX H100 can house up to eight H100 GPUs, each with 80 GB of HBM3 memory, totaling 640 GB of memory and 32 petaflops of performance. Energy efficiency is a priority: data centers aim for a PUE (Power Usage Effectiveness) close to 1.0, using techniques such as free cooling and renewable energy.

Frequently Asked Questions

How many GPUs does a typical data center cluster have?

Clusters can range from tens to thousands of GPUs. For instance, the GPT-4 training cluster used about 25,000 Nvidia A100 GPUs, while smaller data centers may have hundreds.

How are GPU clusters cooled?

Cooling can be air-based (precision air conditioning) or liquid-based (direct-to-chip cold plates or immersion). Liquid cooling is more efficient for high power densities.

Why are GPU clusters important for artificial intelligence?

GPUs perform parallel matrix operations, essential for training deep neural networks. Without clusters, models like GPT-4 or Stable Diffusion would take months or years to train.