A100 PCIe 80GB vs H100 SXM5

The H100's FP16 performance of 1979 TFLOPS dwarfs the A100's 312 TFLOPS, enabling up to 6 times faster large model training where mixed precision dominates. FP32 jumps from 19.5 TFLOPS to 67 TFLOPS support scientific simulations and precision-bound workloads with 3.4 times the throughput. The H100's FP8 capability at 3958 TFLOPS further accelerates quantized inference, reducing latency in deployment scenarios.

Memory bandwidth defines real-world scalability: H100's 3350 GB/s versus A100's 2039 GB/s permits 64 percent larger batch sizes, minimizing overhead in data-heavy training and allowing models with billions of parameters to process faster without swapping. Higher TDP of 700W on H100 versus 400W on A100 correlates with sustained peaks, though it demands robust cooling. PCIe 4.0 on A100 lags PCIe 5.0 on H100 for multi-GPU setups, enhancing interconnect efficiency.

The A100 PCIe 80GB suits cost-conscious deployments where average pricing of $2.11 per hour across 27 offers undercuts H100's $3.54. Its 400W TDP fits power-limited environments, and PCIe form factor integrates seamlessly into standard servers without SXM5 infrastructure. Legacy Ampere-optimized codebases run efficiently at 312 TFLOPS FP16 without Hopper-specific recompilation.

Opt for the H100 SXM5 in performance-critical AI pipelines leveraging 1979 TFLOPS FP16 for rapid LLM training. Its 3350 GB/s bandwidth handles massive datasets, and FP8 at 3958 TFLOPS optimizes inference throughput. NVLink and PCIe 5.0 enable scalable clusters despite 700W TDP and higher $3.54 average hourly cost.