H100 NVL vs Tesla P100

The H100 NVL's FP16 performance of 1979 TFLOPS vastly outpaces the P100's 9.3 TFLOPS: this disparity accelerates mixed-precision training for deep learning models, reducing epoch times from days to hours on large datasets. Its FP32 performance of 67 TFLOPS also exceeds the P100's 9.3 TFLOPS, benefiting general-purpose simulations and inference tasks requiring higher precision.

Memory bandwidth defines a key bottleneck: the H100 NVL's 3350 GB per second enables larger batch sizes and faster data movement compared to the P100's 732 GB per second. This allows the H100 NVL to process models with billions of parameters without excessive swapping, ideal for transformer-based architectures.

Power consumption underscores efficiency differences: the H100 NVL draws 700W TDP versus the P100's 250W, demanding robust cooling and power supplies but delivering over 200 times the FP16 throughput per watt in AI scenarios. Interconnects like NVLink and PCIe 5.0 on the H100 NVL further enhance multi-GPU scaling over the P100's NVLink.

The H100 NVL excels in demanding AI training and inference workloads: its 80 to 94 GB HBM3 VRAM and 1979 TFLOPS FP16 performance handle large language models and generative AI without compromise. Enterprises prioritizing speed and scalability choose it for production environments where cloud pricing of $1.40 to $2.89 per hour justifies the investment.

High-throughput scientific computing also favors the H100 NVL due to its 3350 GB per second bandwidth and FP8 capability of 3958 TFLOPS.

The P100 suits legacy applications and budget-constrained setups: its 16 GB HBM2 VRAM and 9.3 TFLOPS across FP16 and FP32 suffice for older machine learning models or small-scale inference. At $0.60 per hour, it offers low-cost entry for testing or non-critical tasks without high power demands of 250W TDP.

Developers maintaining Pascal-era codebases select the P100 for compatibility in PCIe or SXM2 form factors.