Short answer: A100 80GB and H100 80GB have the same nominal memory capacity, so H100 does not make a model fit merely by changing generations. H100 moves from Ampere compute capability 8.0 to Hopper 9.0, adds the Transformer Engine and higher published bandwidth, but requires a newer software target and can demand substantially more power in SXM systems. Compare the exact PCIe or SXM product—not just “A100 vs H100.”
A100 80GB vs H100 80GB SXM
| Specification | A100 80GB SXM | H100 80GB SXM |
|---|---|---|
| Architecture / compute capability | Ampere / 8.0 | Hopper / 9.0 |
| Memory | 80GB HBM2e | 80GB HBM3 |
| Published bandwidth | 2,039GB/s | 3.35TB/s |
| Maximum configurable power | 400W standard; selected CTS configurations up to 500W | Up to 700W |
| MIG | Up to seven 10GB instances | Up to seven 10GB instances |
| Interconnect | NVLink 600GB/s | NVLink 900GB/s |
| Platform | HGX A100 / DGX A100 class | HGX H100 / DGX H100 class |
The 80GB model-fit ceiling does not change
Both GPUs expose the same nominal capacity. Count the exact weights or model artifact, KV cache, runtime workspaces, context, batch size and concurrency. If a workload needs more than usable 80GB, H100 80GB does not solve the capacity problem; consider a smaller quantization, offload, supported multi-GPU placement or a larger-memory tier.
Use the 48GB vs 80GB guide and 70B VRAM planner. For the individual products, see the A100 40GB/80GB guide and H100 PCIe/SXM/NVL guide.
PCIe and SXM comparisons must stay separate
A100 80GB exists as PCIe and SXM. H100 listings can refer to 80GB PCIe, 80GB SXM or the later 94GB H100 NVL PCIe product. An SXM module is not a PCIe card, and a bare module requires the matching HGX/DGX-class baseboard, cooling, power and firmware.
- A100 80GB PCIe: NVIDIA lists 1,935GB/s bandwidth and 300W power.
- H100 80GB PCIe: NVIDIA’s board specification lists passive cooling and up to 350W depending on power mode.
- H100 NVL: a distinct 94GB PCIe product; do not substitute its 3.9TB/s and 350–400W figures for H100 80GB.
Software support is a generation decision
A100 targets compute capability 8.0; H100 targets 9.0. Confirm the host driver, CUDA toolkit, framework build, runtime, quantization kernels, communication libraries and compiled extensions. H100’s Transformer Engine and FP8 hardware matter only when the software and workload use them correctly. A generic “CUDA supported” label is insufficient.
Does bandwidth predict inference speed?
No. H100 SXM’s 3.35TB/s and A100 SXM’s 2,039GB/s are peak hardware specifications. Tokens per second also depend on model architecture, quantization, kernels, prompt processing, context, batch size, parallelism, CPU and complete-system configuration. Do not multiply an A100 measurement by the bandwidth ratio.
Use the same reproducible workload on accessible systems and share the complete configuration rather than an isolated speed number.
MIG and multi-GPU caveats
Both products support MIG. A process assigned a 10GB slice does not see the full 80GB physical GPU. NVLink improves supported GPU-to-GPU communication but does not create one automatic pooled-memory device; the runtime must shard or parallelize the workload. Read the multi-GPU VRAM guide.
Choose A100 when
- The existing software stack, server qualification and support path are proven for Ampere.
- The exact workload fits inside 80GB with headroom.
- A complete tested A100 system provides the required service level and defensible total cost.
Choose H100 when
- The application measurably benefits from Hopper-supported kernels or precision modes.
- The driver, CUDA and framework stack are validated for compute capability 9.0.
- The higher platform power, cooling and acquisition requirements are justified by measured workload results.
Official NVIDIA sources
- NVIDIA A100 product specifications
- NVIDIA H100 product specifications
- NVIDIA H100 PCIe board specification
- NVIDIA CUDA compute-capability table
- NVIDIA Hopper architecture overview
Reviewed October 25, 2026. Specifications are sourced from NVIDIA. HighVRAM has not independently benchmarked A100 and H100 under one controlled workload. Qualified configurations, support, availability and prices change.
Looking beyond Hopper? Compare H100 and B200 for the 80GB-to-180GB architecture and capacity transition.
Choosing specifically within the 80GB tier? Use the Best 80GB GPU for local AI buyer guide for an equal-capacity A100-versus-H100 decision, exact Qwen3.5 boundaries and a reproducible platform test.