NVIDIA H100 vs A100: Which Should You Choose?

The A100 (Ampere, 2020) and H100 (Hopper, 2022) represent two generations of NVIDIA datacenter GPUs. While the A100 remains highly capable, the H100 introduces significant architectural improvements for AI workloads.

Key H100 improvements: Transformer Engine with FP8 support, 4th gen Tensor Cores, HBM3 memory (vs HBM2e), and higher memory bandwidth.

Memory: A100 offers 40GB or 80GB HBM2e. H100 provides 80GB HBM3 with 3.35 TB/s bandwidth (vs 2 TB/s on A100 80GB).

Compute: H100 delivers ~3x the FP16 performance and ~6x FP8 performance compared to A100.

Interconnect: Both support NVLink, but H100's NVLink 4.0 offers 900 GB/s vs 600 GB/s on A100.

Power: H100 SXM draws 700W vs 400W for A100 SXM. Consider cooling and power infrastructure.

LLM Training: H100 shows 2-3x speedup for transformer training, with FP8 training providing additional gains.

LLM Inference: H100 delivers 2-4x higher throughput, especially with FP8 inference.

Diffusion Models: ~2x faster training and inference on H100.

Traditional ML: Smaller gains (1.3-1.5x) for non-transformer workloads.

A100 80GB: $15,000-20,000 new, $8,000-12,000 used. Good availability.

H100 SXM: $30,000-40,000 new, limited used market. Allocation constraints easing.

Cloud pricing: H100 instances typically 2-2.5x the cost of A100 instances.

ROI consideration: H100's higher throughput often justifies the premium for production workloads.

Choose A100 if: Budget constrained, workloads don't benefit from FP8, building on used market, or existing A100 infrastructure.

Choose H100 if: Maximum performance needed, FP8 training/inference beneficial, building new infrastructure, or cloud cost optimization through faster completion.

Consider alternatives: AMD MI300X offers competitive performance at lower cost. For inference-only, consider specialized accelerators.