H100 vs. H200 vs. B200: which GPU should you use?

H100, H200, and B200 GPUs each provide distinct advantages based on memory, compute, and cost, catering to varying AI inference needs. The choice of GPU affects model latency, throughput, and cost, with the H100 being ideal for smaller models and sporadic traffic through its cost-effective Multi-Instance GPU (MIG) capability, the H200 accommodating very large models like DeepSeek-R1 due to its extensive memory capacity, and the B200 excelling in high-throughput production inference with its FP4 support and superior memory bandwidth. These GPUs utilize SXM connections for faster GPU interactions and NVLink for efficient weight and activation transfers, crucial for running large models across multiple GPUs. Additionally, innovations like the Blackwell architecture's FP4 and Tensor Memory Accelerator enhance memory efficiency and throughput, while asynchronous programming optimizes data movement, reducing idle times during inference. The optimal GPU choice hinges on specific AI workload requirements, such as model size, traffic volume, and budget considerations.

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