GPU cluster in cooling data center

GPU cluster in a cooled data center powers large-scale AI model training.

About this subject

GPU clusters are the backbone of training artificial intelligence models, such as large language models (LLMs). In a cooled data center, dozens or hundreds of GPUs work in parallel, processing terabytes of data to adjust billions of parameters. Cooling is critical: modern GPUs like the NVIDIA H100 can consume up to 700W each, generating intense heat. Liquid cooling or precision air conditioning keeps temperatures between 18°C and 27°C, preventing throttling and failures.

Brazil hosts data centers from global operators like Equinix and Ascenty, offering infrastructure for GPU clusters. The AI market in the country grows at double-digit rates, driven by startups and research institutions. Strategic location, with submarine cables connecting South America, reduces latency for clients in the Western Hemisphere.

Trivia: training GPT-3 (175 billion parameters) consumed about 1,287 MWh of energy, equivalent to the annual consumption of 120 Brazilian homes. Efficient clusters use interconnects like NVLink and InfiniBand to minimize communication bottlenecks between GPUs.

Frequently Asked Questions

How many GPUs are needed to train a model like GPT-4?

It is estimated that GPT-4 was trained with tens of thousands of GPUs (possibly 25,000 or more) of NVIDIA A100 or H100, over months.

What is the difference between liquid and air cooling in data centers?

Liquid cooling is more efficient for dissipating high thermal loads, using water or dielectric fluid. Air cooling is simpler and cheaper, but less effective in dense clusters.

Does Brazil have data centers with GPU clusters?

Yes, operators such as Equinix, Ascenty, and ODATA offer data centers in Brazil with capacity for GPU clusters, serving technology and research companies.