H100 SXM5 vs Tesla P100

Memory capacity and bandwidth define workload feasibility: the H100 SXM5's 80 to 94 GB HBM3 VRAM supports massive batch sizes in deep learning, far exceeding the P100's 16 GB HBM2 limit which constrains models to smaller datasets. The H100's 3350 GB/s bandwidth enables rapid data movement, reducing bottlenecks in memory-intensive operations, whereas the P100's 732 GB/s often leads to stalls with contemporary model sizes.

Floating-point performance reveals training and inference implications. The H100 SXM5's 1979 TFLOPS FP16 vastly accelerates mixed-precision training common in large language models, while its 67 TFLOPS FP32 suits precise scientific simulations; the P100 matches 9.3 TFLOPS across FP16 and FP32, adequate for 2016-era tasks but insufficient for scaled modern pipelines. FP8 at 3958 TFLOPS on H100 further optimizes inference latency. Higher 700W TDP on H100 demands robust cooling, contrasting P100's efficient 250W profile for lighter deployments.

The H100 SXM5 excels in demanding AI applications: its 80 to 94 GB VRAM handles large-scale LLM training and inference, where the 1979 TFLOPS FP16 throughput cuts epochs dramatically. Users prioritizing speed over cost in cloud environments benefit from 32 live offers starting at $0.80 per hour, despite the $3.54 average.

The P100 suits legacy or low-budget scenarios: its 16 GB VRAM and 9.3 TFLOPS FP16 suffice for older scientific computing or fine-tuning small models without exceeding 250W TDP constraints. At a fixed $0.60 per hour from one offer, it provides economical access for compatibility-bound workflows avoiding H100's 700W power and higher costs.