A100 PCIe 80GB vs H100 NVL

The H100 NVL vastly outperforms the A100 PCIe 80GB in raw compute: 1979 TFLOPS FP16 versus 312 TFLOPS accelerates deep learning training by up to 6x in mixed precision workflows. FP32 jumps from 19.5 TFLOPS to 67 TFLOPS, benefiting simulation-heavy tasks in scientific computing. The introduction of FP8 at 3958 TFLOPS on H100 optimizes large language model inference, reducing latency for high-throughput serving. Memory bandwidth doubles from 2039 GB/s to 3350 GB/s on H100, enabling larger batch sizes in training: models with 80 GB VRAM on A100 handle modest batches, while H100's 94 GB HBM3 and superior bandwidth support massive datasets without spilling to slower storage. Higher TDP of 700W versus 400W reflects H100's dense compute but demands robust cooling in cloud deployments.

Opt for the A100 PCIe 80GB in cost-sensitive environments where its $0.89 per hour starting price and $2.08 average beat H100's $1.40 to $2.89 range. It suits legacy Ampere-optimized codebases or workloads not fully leveraging Hopper features, with 28 live offers ensuring broad availability. Lower 400W TDP fits power-constrained clusters, and 80 GB HBM2e handles standard LLM fine-tuning or Stable Diffusion without overkill.

Choose the H100 NVL for peak performance in modern AI pipelines demanding Hopper's 1979 TFLOPS FP16 or 3958 TFLOPS FP8. Its 3350 GB/s bandwidth excels in large-batch training of LLMs exceeding 70B parameters. Despite fewer offers at 9, it future-proofs investments for inference at scale.