H100 vs A100 comparison: Best GPU for LLMs, vision models, and scalable training

NVIDIA's A100 and H100 GPUs cater to distinct deep learning needs, with the A100 being the go-to for stable, large-scale training and inference due to its Ampere architecture, third-generation Tensor Cores, and HBM2e memory. It supports a broad range of precisions and is cost-efficient for production environments. In contrast, the H100, built on the Hopper architecture, is designed for cutting-edge workloads, particularly large language models (LLMs) and transformer-heavy applications. It features fourth-generation Tensor Cores, FP8 precision support, HBM3 memory, and enhanced bandwidth, making it ideal for reducing training times and handling larger models. While the A100 remains a cost-effective and reliable choice for various AI/ML tasks, the H100 excels in scenarios requiring maximum performance and efficiency at scale, despite its higher operational costs. Northflank offers both GPUs for flexible cloud deployment, allowing teams to choose based on specific workload requirements and budget considerations.