H100 vs A100: Which GPU Should You Use for LLM Training in 2026?

The NVIDIA H100 and A100 are the two most widely deployed data center GPUs for large language model training. The H100 (Hopper architecture, 2022) delivers roughly 3× the FP16 throughput of the A100 (Ampere, 2020) and introduces FP8 training support — a major advantage for modern LLM training pipelines. However, the A100 remains widely available at lower cost and has a mature software ecosystem.

For most LLM training workloads in 2026, the H100 is the clear winner on raw performance. The question is whether the cost premium is justified for your specific workload size and timeline. Check live H100 and A100 cloud pricing to compare current on-demand and spot rates across providers.

The H100 SXM5 delivers 989 TFLOPS FP16 vs the A100's 312 TFLOPS — a 3.2× improvement. In FP8 (supported natively on H100), throughput reaches 1,979 TFLOPS. Memory bandwidth improves from 2,039 GB/s (A100 HBM2e) to 3,350 GB/s (H100 HBM3), a 1.6× gain that matters significantly for memory-bound transformer attention layers.

In practice, real-world LLM training throughput improvements are typically 2–2.5× rather than the theoretical 3.2×, due to memory bandwidth bottlenecks, communication overhead in multi-GPU setups, and framework overhead. MLPerf benchmarks show H100 training GPT-3 175B approximately 2.3× faster than A100 in equivalent cluster configurations. See the H100 vs A100 spec comparison for a full side-by-side breakdown.

As of July 2026, H100 80GB on-demand pricing ranges from $2.49/hr (RunPod spot) to $5.89/hr (AWS on-demand). A100 80GB ranges from $1.29/hr (RunPod spot) to $3.67/hr (AWS on-demand). The H100 commands roughly a 1.8–2× price premium over A100.

Given the 2–2.5× real-world training speedup, the H100 typically delivers better cost-per-token-trained than the A100 — especially for large models (>30B parameters) where memory bandwidth is the primary bottleneck. For smaller models (<7B parameters), the A100 may offer better cost efficiency due to lower utilization of H100's bandwidth advantages. Use the GPU price comparison table to see current rates from all 94+ providers side-by-side.

Choose H100 when: (1) training models >30B parameters where memory bandwidth is the bottleneck; (2) you need FP8 training to reduce memory footprint and increase throughput; (3) your timeline is fixed and faster training reduces total cost; (4) you're running multi-GPU clusters where NVLink 4.0's 900 GB/s bidirectional bandwidth (vs A100's 600 GB/s) reduces all-reduce communication overhead.

H100 is also the better choice for inference serving of large models — its higher memory bandwidth enables lower latency per token at high batch sizes. Compare H100 cloud providers to find the best on-demand rate for your region.

Choose A100 when: (1) training smaller models (<13B parameters) where H100's bandwidth advantage is less pronounced; (2) budget is the primary constraint and you can tolerate longer training runs; (3) you need maximum availability — A100 instances are available from more providers and regions than H100; (4) your existing infrastructure and CUDA code is optimized for Ampere and you want to avoid migration risk.

A100 also makes sense for inference workloads where model size fits comfortably in 40GB or 80GB VRAM and latency requirements are moderate. Track A100 price trends over time to see how costs have moved.

A100 80GB is available from 40+ providers including AWS (p4d.24xlarge), Google Cloud (a2-highgpu), Azure (NDv4), CoreWeave, Lambda Labs, RunPod, and most GPU cloud marketplaces. H100 80GB is available from 30+ providers but with tighter supply — CoreWeave, Lambda Labs, Nebius AI, and Crusoe Cloud typically have the best H100 availability outside hyperscalers.

For reserved capacity at scale, AWS, Google Cloud, and Azure offer 1-year and 3-year committed use contracts for both GPU types. Specialist providers like CoreWeave offer cluster reservations with dedicated networking. Compare CoreWeave vs Lambda Labs pricing or RunPod vs Vast.ai pricing to find the best fit for your workload.