H100 vs A100: Specs, AI Performance, and Rental Cost
NVIDIA H100 and A100 are datacenter GPUs for training, fine-tuning, and inference, but they target different performance and budget tiers. Compare architecture, memory bandwidth, workload fit, and total rental cost before choosing one.
H100 vs A100 specifications
| Specification | H100 SXM | A100 80 GB SXM |
|---|---|---|
| Architecture | Hopper | Ampere |
| GPU memory | 80 GB HBM3 | 80 GB HBM2e |
| Memory bandwidth | 3.35 TB/s | 2.039 TB/s |
| Low-precision AI | FP8 Transformer Engine | FP16, BF16, TF32, INT8 |
| MIG support | Up to 7 instances | Up to 7 instances |
Specifications vary by PCIe, SXM, and NVL form factor. Check the exact GPU attached to a rental listing before comparing price or expected throughput.
Which GPU should you choose?
Choose A100 when 80 GB of VRAM is the main requirement and predictable fine-tuning, notebooks, or validation matter more than maximum throughput. Choose H100 when training speed, long-context inference, high concurrency, or FP8 support can reduce total job time enough to justify the premium.
- Fine-tuning: A100 is often the value baseline; H100 is useful when turnaround time is the constraint.
- LLM inference: H100 is better suited to high-throughput and latency-sensitive serving.
- Experiments: Both may be excessive when an RTX 4090 or hosted model API fits the workload.
- Long context: benchmark production-shaped sequences because KV cache and concurrency can dominate memory use.
H100 vs A100 rental cost in India
Do not compare only the hourly price. Multiply the live INR rate by model loading, training, checkpointing, retries, and expected completion time. A faster H100 can cost less for a throughput-bound job, while A100 usually wins when the workload already meets its deadline.
Compare live H100 and A100 availability Plan GPU rental pricing in India