The H100 and A100 are the two most rented GPUs in cloud AI infrastructure. Both are NVIDIA data center GPUs with 80 GB of HBM memory, but the H100 is a full generation newer — and priced accordingly. Choosing between them is one of the most common questions when budgeting an AI workload.
Specs at a Glance
| Spec | H100 SXM5 | A100 SXM4 |
|---|---|---|
| Architecture | Hopper (2022) | Ampere (2020) |
| VRAM | 80 GB HBM3 | 80 GB HBM2e |
| Memory Bandwidth | 3.35 TB/s | 2.0 TB/s |
| FP16 TFLOPs | 1,979 (with sparsity) | 312 (dense) |
| NVLink Bandwidth | 900 GB/s | 600 GB/s |
| TDP | 700 W | 400 W |
| Typical Cloud Price | $2.49–$4.00/hr | $1.49–$2.20/hr |
Performance: Where the H100 Wins
The H100 is not merely an incremental upgrade — it is a different class of GPU. The Transformer Engine in Hopper architecture introduces FP8 mixed-precision compute, which cuts memory bandwidth requirements roughly in half for large language model training. For a 70B parameter model training run, the H100 SXM5 completes epochs 2.5–3× faster than an A100 SXM4 on equivalent tasks.
Memory bandwidth is the critical bottleneck in LLM training. The H100's 3.35 TB/s (vs A100's 2.0 TB/s) directly translates to faster gradient computation and activation checkpointing. For multi-GPU runs with NVLink, the H100's 900 GB/s all-reduce bandwidth nearly eliminates communication overhead that plagues A100 multi-node setups.
Pricing: The Real Cost Difference
H100s currently rent for $2.49–$4.00/hr on major cloud providers, versus $1.49–$2.20/hr for A100s. That's roughly 60–80% more expensive per GPU-hour. But the comparison needs to account for throughput: if the H100 trains 2.5× faster, the cost-per-training-step is actually lower on an H100 for large models.
When to Choose the H100
- →Training or fine-tuning models ≥ 13B parameters (Llama 3 70B, Mistral Large, etc.)
- →Multi-GPU training runs where NVLink interconnect bandwidth matters
- →Inference serving at high throughput (H100 Tensor Core throughput is ~3× higher)
- →Time-sensitive experiments where faster iteration speed justifies cost
- →Any workload using FlashAttention-2 or FP8 quantization — H100 gets full benefit
When to Choose the A100
- →Fine-tuning smaller models (7B–13B) with QLoRA or LoRA — A100 80GB has enough headroom
- →Inference for mid-size models where you need 80 GB VRAM but not maximum throughput
- →Budget-constrained experiments and prototyping
- →Workloads that are I/O-bound rather than compute-bound (the bandwidth gap matters less)
- →When H100 availability is limited and you need to start now
Provider Comparison for H100 and A100
H100 availability has expanded significantly in 2025. Lambda Labs, CoreWeave, RunPod, and Hyperstack all offer H100 SXM5 instances. A100s are more widely available and often immediately accessible without waitlists.
The Verdict
For production LLM training at scale, the H100 wins on cost-per-FLOP even at its higher hourly rate. For development, fine-tuning smaller models, or inference workloads where you don't need peak throughput, the A100 remains excellent value. The best approach: benchmark your specific workload on a single H100 vs A100 for a short run, calculate the cost per epoch or per token, then commit to the cheaper option at scale.