H100 vs P100

H100's FP16 performance of 1979 TFLOPS towers over P100's 9.3 TFLOPS, enabling dramatically faster neural network training where half-precision computations dominate: training times shrink by factors exceeding 200 times for compatible workloads. P100 maintains parity between FP16 and FP32 at 9.3 TFLOPS each, suiting balanced general-purpose computing from its era, but H100's FP32 of 67 TFLOPS still outpaces it by over sevenfold while excelling in FP8 at 3958 TFLOPS for inference optimization. This FP16 to FP32 delta on H100 signals specialization for modern machine learning pipelines, accelerating mixed-precision training. Memory bandwidth defines practical limits: H100's 3350 GB/s supports batch sizes up to five times larger than P100's 732 GB/s constraint, reducing overhead in data loading for large language models and allowing full-model training without excessive slicing. Power draw compounds differences, with H100's 700W TDP demanding robust cooling versus P100's efficient 250W, impacting cluster density but justifying throughput gains. Overall, H100 transforms inference latency, cutting it from minutes to seconds on equivalent tasks due to VRAM capacity of 80 to 94 GB enabling in-GPU processing of models infeasible on 16 GB.

Opt for H100 in scenarios demanding high-throughput AI training or inference, such as developing large language models requiring over 16 GB VRAM: its 80 to 94 GB HBM3 handles full-parameter loading, avoiding multi-GPU complexity. Deploy H100 for FP16-heavy workloads like Stable Diffusion generation, where 1979 TFLOPS yields outputs 200 times faster than P100's 9.3 TFLOPS. Cloud users prioritizing speed over cost across 56 providers from $0.80 per hour benefit from NVLink and PCIe 5.0 interconnects scaling to multi-node clusters.

Select P100 for budget-constrained legacy applications or prototyping, where its $0.07 per hour starting price and 250W TDP minimize operational costs across sparse 3 cloud offers. It suffices for FP32-bound scientific simulations at 9.3 TFLOPS matching its FP16, or small-scale inference on models fitting 16 GB HBM2 without needing 3350 GB/s bandwidth. Compatibility with older NVLink and PCIe form factors aids quick migrations from on-premises Pascal-era setups.