H100 PCIe vs Tesla P100

The H100's FP16 performance reaches 1979 TFLOPS, dwarfing the P100's 9.3 TFLOPS: this disparity accelerates deep learning training, where half-precision computations dominate, enabling the H100 to process models orders of magnitude faster. In contrast, the P100's equal 9.3 TFLOPS for FP16 and FP32 limits it to smaller-scale training or FP32-centric tasks like traditional simulations.

Memory specifications define practical limits: the H100's 80 to 94 GB HBM3 supports massive batch sizes for large language models, while the P100's 16 GB HBM2 restricts batches and model sizes, often requiring gradient accumulation that slows workflows. Bandwidth reinforces this: 3350 GB/s on the H100 sustains high-throughput data movement versus the P100's 732 GB/s, reducing bottlenecks in inference pipelines.

Power draw further differentiates usage: the H100's 700W TDP suits dense data centers, but demands robust cooling, whereas the P100's 250W fits edge or low-power setups without compromising older architectures' efficiency.

The H100 excels in demanding AI applications: its 1979 TFLOPS FP16 and 80 to 94 GB VRAM handle large-scale LLM training and inference, where the P100's 9.3 TFLOPS and 16 GB fall short. Cloud users prioritize it for tasks needing 3350 GB/s bandwidth to maximize batch sizes and throughput at $1.25 per hour starting rates.

High-performance computing clusters favor the H100's FP8 at 3958 TFLOPS and PCIe 5.0 support for rapid scaling across nodes.

The P100 suits budget-conscious legacy deployments: at $0.60 per hour, its 9.3 TFLOPS FP32 matches many scientific simulations from the Pascal era without the H100's 700W power overhead. It integrates seamlessly into existing NVLink-based systems running unoptimized older codebases.

Light inference or prototyping benefits from the P100's 250W TDP and 16 GB HBM2, avoiding overprovisioning for workloads insensitive to modern precision formats.