All Comparisons
gpu comparison
NVIDIA H100 vs A100: Worth the Upgrade for AI?
Performance, cost, and ROI comparison of upgrading from A100 to H100 GPUs.
Overview
The NVIDIA A100 has been the workhorse of AI infrastructure since 2020, but the H100 (released 2023) offers 4x faster training and 30x faster inference. This comparison helps you decide if upgrading is worth the investment.
NVIDIA A100
Pros
- Lower cost (especially used market)
- Mature software ecosystem
- 40GB and 80GB variants
- Good for inference and fine-tuning
Cons
- Older Ampere architecture
- Slower training (3-4x vs H100)
- Lower memory bandwidth
- Being phased out
Key Specs
ArchitectureAmpere
Memory40GB or 80GB HBM2e
Bandwidth1.55 TB/s (80GB) / 1.94 TB/s (40GB)
FP16 Performance624 TFLOPS (with sparsity)
TDP400W
InterconnectNVLink 3.0 (600 GB/s)
NVIDIA H100
Pros
- 4x faster training than A100
- 30x faster inference (with FP8)
- Transformer Engine for LLMs
- Industry standard for new deployments
Cons
- Higher cost per GPU
- Requires newer datacenter infrastructure
- 700W TDP requires liquid cooling
Key Specs
ArchitectureHopper
Memory80GB HBM3
Bandwidth3.35 TB/s
FP16 Performance1,979 TFLOPS
TDP700W
InterconnectNVLink 4.0 (900 GB/s)
Verdict
For new AI training deployments, H100 is the clear choice — 4x faster training means 4x lower total cost despite higher per-GPU cost. For inference-only workloads on smaller models, A100 may still be cost-effective. Harch Corp offers both A100 and H100 GPU cloud instances.