AGAnchorGPU

H100 SXM vs A100 SXM

H100 SXM vs A100 SXM: specs and rental cost

Compare memory, node configurations, and fixed weekly and monthly cost before choosing between NVIDIA H100 SXM and NVIDIA A100 SXM.

Decision summary

H100 SXM provides the same amount of VRAM per card. A100 SXM costs $560 less per 30-day term . Choose based on whether memory headroom or lower fixed cost is the binding constraint.

NVIDIA

H100 SXM

training · high-throughput inference

$1,352/ 30 days
View GPU
VS
NVIDIA

A100 SXM

training · batch inference

$792/ 30 days
View GPU
SpecificationH100 SXMA100 SXM
ArchitectureHopperAmpere
VRAM80 GB HBM380 GB HBM2e
Host resources / card24 vCPU · 192 GB RAM20 vCPU · 160 GB RAM
Local NVMe / card3.84 TB2 TB
7-day price / card$380$224
30-day price / card$1,352$792
4-GPU node / 30 days$4,975$2,915
Catalog quantity690 cards780 cards

Workload decision · updated 2026-09-04

H100 SXM vs A100 SXM: is Hopper worth the premium?

Both accelerators offer 80 GB, so this is not primarily a memory-capacity decision. Compare the value of a Hopper-optimized software path with the lower fixed cost of an established Ampere configuration.

01

Choose H100 SXM when…

Choose H100 SXM when your application has a validated Hopper path, such as kernels deliberately using Transformer Engine or FP8, or when profiling identifies memory movement as a bottleneck. Keep the accuracy target and software versions the same in your pilot.

02

Choose A100 SXM when…

Choose A100 SXM when the complete workload already fits in 80 GB, the CUDA environment is proven on Ampere, and a pilot has not demonstrated enough benefit from Hopper to cover the higher rental cost.

Manufacturer specificationH100 SXMA100 SXM
ArchitectureHopperAmpere
Memory80 GB HBM380 GB HBM2e
Published memory bandwidth3.35 TB/s2,039 GB/s
Published SXM NVLink bandwidth900 GB/s600 GB/s

Published accelerator specifications, not a measurement of AnchorGPU infrastructure. Host topology and delivered fabric must be confirmed separately.

What to verify in a pilot

  • Switching between these models does not increase the 80 GB capacity.
  • FP8-capable hardware does not prove that your model, kernels or quality target can use FP8.
  • Multi-GPU scaling requires an explicit parallelism strategy and a verified host fabric.
  • Compare peak memory, useful step throughput, communication time and validation quality with the same workload.

Bottom line. Start with A100 as the lower-cost 80 GB baseline. Choose H100 when an application-level pilot shows a useful Hopper advantage.

Catalog cost difference: $156 per 7 days and $560 per 30 days. Prices are read from the same catalog as the configurator.

Sources & method

Manufacturer specifications inform the comparison. Recommendations are workload-dependent; no AnchorGPU performance benchmark is claimed.

NVIDIA H100 specificationsNVIDIA A100 specifications