B100 vs H100: Best GPU for LLMs, vision models, and scalable training

NVIDIA's H100 and B100 GPUs represent different stages in AI model training and deployment, with the H100 being a well-established choice for production inference and fine-tuning thanks to its flexibility and support for both PCIe and SXM form factors. The newer B100, built on the Blackwell architecture, is designed for frontier-scale AI workloads, offering advancements such as faster HBM3e memory, dual transformer engines, and improved FP8 performance, which make it suitable for training large models with extended context lengths. While the B100 provides significant performance gains and scalability benefits for new model classes, its limited availability and the need for newer software versions may pose challenges. Meanwhile, the H100 remains a reliable and cost-effective option for teams focused on stable and flexible deployment environments. Northflank, a full-stack AI cloud platform, provides access to GPUs like the H100 to support AI workload deployments without long-term commitments.