H100 and other GPUs — which are relevant for your ML workload?

Igor, the Technical Product Manager for IaaS at Nebius AI, provides an in-depth exploration of NVIDIA's popular GPU chips, including the H100, L4, L40, and A100, discussing their relevance and optimization for transformer neural networks. These GPUs, built on Hopper, Ada Lovelace, Ampere, and Volta microarchitectures, are engineered to support transformer models, which have become the industry standard due to their superior performance in pre-training and fine-tuning tasks. Igor highlights the significance of numerical precision, particularly the FP8 format, in enhancing GPU performance for machine learning tasks, enabling models to be trained and inferred more efficiently. He also touches upon the different types of GPU cores, such as CUDA, Tensor, and RT cores, and their specific applications in deep learning and graphics. The discussion extends to the considerations for choosing between different GPU models based on specific use cases, such as machine learning, high-performance computing, and graphics, emphasizing the balance between cost, performance, and scalability. Nebius AI utilizes these NVIDIA GPUs in their data center in Finland, providing a platform for training, inference, and fine-tuning machine learning models.

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