A100 SXM4 40GB vs H100 PCIe

The H100 PCIe demonstrates superior compute capabilities over the A100 SXM4 40GB. FP16 performance reaches 1979 TFLOPS on the H100 compared to 312 TFLOPS on the A100: this enables faster deep learning training where half-precision arithmetic dominates. FP32 performance improves from 19.5 TFLOPS to 67 TFLOPS, benefiting general-purpose simulations and certain inference workloads. The H100's FP8 capability at 3958 TFLOPS further accelerates quantized inference tasks absent on the A100.

Memory bandwidth profoundly impacts real-world usage. The H100's 3350 GB/s versus the A100's 2039 GB/s supports larger batch sizes in model training, reducing per-iteration time for large language models. With 80 GB HBM3 VRAM against 40 GB HBM2e, the H100 handles bigger models or datasets without swapping to slower host memory. However, the H100's 700W TDP exceeds the A100's 400W, increasing power and cooling demands in clusters.

Interconnect advancements favor the H100. PCIe 5.0 and enhanced NVLink outperform the A100's PCIe 4.0, improving multi-GPU scaling for distributed training.

Opt for the A100 SXM4 40GB in cost-sensitive deployments. Its pricing from $0.13 per hour averages $1.66 per hour, far below the H100's $1.25 per hour starting point and $2.77 per hour average. Lower 400W TDP suits power-constrained environments or legacy clusters.

The A100 suffices for workloads not fully leveraging Hopper features. Applications with models fitting in 40 GB VRAM and requiring up to 312 TFLOPS FP16 perform adequately without the H100's premium cost.

Select the H100 PCIe for cutting-edge AI workloads demanding peak performance. Its 1979 TFLOPS FP16 and 3958 TFLOPS FP8 enable training and inference of massive models infeasible on the A100's 312 TFLOPS FP16.

Scenarios with large datasets benefit from 80 GB HBM3 VRAM and 3350 GB/s bandwidth. The H100 excels in multi-GPU setups via PCIe 5.0 and NVLink, despite higher 700W TDP and pricing from $1.25 per hour.