The NVIDIA H100 (Hopper) introduced FP8 and the Transformer Engine, delivering a large leap over the Ampere-based A100. But the A100 80GB remains cheap, abundant and capable for many AI workloads in 2026.
We compare them on training, inference, memory and total cost so you can decide whether Hopper is worth the premium or Ampere is still the value play.
Key differences at a glance
- SpecH100 SXMA100 80GBArchitecture: H100, Hopper; A100, Ampere
- Memory: H100, 80GB HBM3; A100, 80GB HBM2e
- Bandwidth: H100, 3.35 TB/s; A100, 2.0 TB/s
- Transformer Engine: H100, Yes (FP8); A100, No
- MIG: H100, Yes; A100, Yes
Specifications at a glance
| Spec | H100 SXM | A100 80GB |
|---|---|---|
| Architecture | Hopper | Ampere |
| Memory | 80GB HBM3 | 80GB HBM2e |
| Bandwidth | 3.35 TB/s | 2.0 TB/s |
| Transformer Engine | Yes (FP8) | No |
| MIG | Yes | Yes |
Generational leap
The H100 adds FP8 and the Transformer Engine, delivering multiples of the A100’s throughput on modern transformer training and inference, plus ~67% more bandwidth.
Where the A100 still wins
The A100 is far cheaper per GPU and abundant in the used and cloud markets. For many fine-tuning, classic ML and inference jobs it remains a strong value.
Verdict: which should you buy?
For cutting-edge LLM training/inference at scale, the H100 is worth the premium. For budget-conscious teams, fine-tuning and steady inference, the A100 80GB is still a smart, cost-effective choice.
Total cost, availability and support
Raw benchmarks are only half the story. Street prices, stock levels and lead times for H100 and A100 swing widely with demand and region, and warranty and after-sales support differ from one seller to the next. On GPU Vendors you can compare verified vendors side by side, see transparent pricing and lead times, and buy direct, which often matters more to total cost of ownership than a few percentage points of raw performance.
Which one is right for you?
If your workloads and budget are still growing, favour the option with more memory and longer headroom so you are not forced to upgrade again within a year. If you have a fixed, well-understood workload, the more affordable choice frequently delivers the best value per dollar. Model both against your real requirements, resolution and frame-rate targets for gaming, or model size, batch size and context length for AI, before you commit.
Frequently asked questions
Is H100 worth it over A100?
It depends on your workload and budget. If you regularly hit the limits of A100, whether that is memory capacity, bandwidth or raw throughput, then H100 pays for itself in headroom and longevity. If A100 already covers your needs comfortably, the upgrade is harder to justify on performance alone.
Will A100 still be supported?
Yes. Both options continue to receive driver and software support, so A100 remains a viable choice and is often the better value on the used and clearance market.
Where can I compare prices for H100 and A100?
Use the GPU Vendors comparison tool to line up full specs and live prices from verified sellers, then buy direct from the vendor with the best price and lead time.
Prices and availability change constantly. Compare live offers from verified sellers and build a side-by-side spec sheet on the GPU Vendors comparison tool.