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NVIDIA A100 vs NVIDIA H100 SXM: Spec Comparison & Buyer's Guide

In AI infrastructure selection, NVIDIA A100 and NVIDIA H100 SXM are two accelerators frequently compared. This article contrasts them item by item — architecture, compute, memory, power, and release cadence — to help you quickly judge which fits training or inference workloads.

Spec Comparison Table​

VendorNVIDIA A100NVIDIA H100 SXM
VendorNVIDIANVIDIA
ArchitectureAmpere GA100Hopper GH100
ProcessTSMC 7nmTSMC 4N
Release Date2020 6 GTC2022 3 GTC
FP8 Compute—3,958 TFLOPS
FP16 Compute——
FP32 Compute19.5 TFLOPS67 TFLOPS
INT8 Compute——
Memory Type——
Memory Capacity80 GB HBM2e80 GB HBM3
Memory Bandwidth1,935 GB/s3.35 TB/s
TDP Power300 W / 400 W700 W

Key Differences​

  • Power: NVIDIA A100 has a TDP of 300 W / 400 W, lower than NVIDIA H100 SXM's 700 W, friendlier to datacenter PUE and cooling.

Selection Advice​

  • When chasing extreme single-card compute and a mature toolchain, prioritize NVIDIA A100; if budget, power wall, or local support are hard constraints, NVIDIA H100 SXM often fits better. Use this site's AI Compute Card Comparison Tool to validate multiple chips side-by-side before deciding.

FAQ​

What are the main differences between NVIDIA A100 and NVIDIA H100 SXM?​

The core difference is architecture and compute density: NVIDIA A100 uses Ampere GA100, FP8 ~No public FP8 data, memory 80 GB HBM2e; NVIDIA H100 SXM uses Hopper GH100, FP8 ~3,958 TFLOPS, memory 80 GB HBM3. See the comparison table above.

What is the TDP (power) of NVIDIA A100?​

NVIDIA A100 has a TDP of 300 W / 400 W; actual whole-system power also includes board, fans, and PUE.

Which is better for large-model training / inference?​

Training values memory capacity, bandwidth, and multi-card interconnect; inference values single-card throughput and power efficiency. Combine the "Key Differences" and "Selection Advice" above with your batch size, model size, and SLA.

How much do NVIDIA A100 and NVIDIA H100 SXM differ in memory capacity?​

NVIDIA A100 is 80 GB HBM2e, NVIDIA H100 SXM is 80 GB HBM3; the gap directly affects loadable model size and context length.