Comparison

NVIDIA H100 vs NVIDIA A100 80GB

Hopper vs Ampere — the generational leap

The H100 delivers 3.2x the FP16 performance of the A100 80GB (990 vs 312 TFLOPS) with faster HBM3 memory. The A100 remains cost-effective at roughly half the price per hour.

Pricing Comparison

Specifications

Specification	NVIDIA H100	NVIDIA A100 80GB
Manufacturer	NVIDIA	NVIDIA
Architecture	Hopper	Ampere
Accelerator Type	GPU	GPU
Primary Use	training	training
Memory (VRAM)	80 GB	80 GB
FP16 Performance	990 TFLOPS	312 TFLOPS
TDP	700W	400W
Perf per Watt	1.41 TFLOPS/W	0.78 TFLOPS/W

Detailed Analysis

The NVIDIA H100 and A100 80GB represent two generations of data centre GPU architecture. The H100, based on Hopper, introduced the Transformer Engine with FP8 precision support, delivering a step change in performance for transformer-based models.

In raw compute, the H100's 990 FP16 TFLOPS triples the A100's 312 TFLOPS. Memory bandwidth also improves significantly — 3.35 TB/s (HBM3) vs 2.0 TB/s (HBM2e) — making the H100 faster on memory-bound workloads like large language model training.

However, the A100 80GB remains highly relevant. At roughly half the cloud cost per hour, it offers superior price/performance for workloads that don't require maximum throughput. Fine-tuning, medium-scale training, and batch inference can all run cost-effectively on A100s.

The A100 also pioneered Multi-Instance GPU (MIG) technology, which remains valuable for serving multiple smaller models on a single GPU. Both GPUs share 80GB of memory, though the H100's faster HBM3 gives it an edge on memory-throughput-sensitive workloads.

Verdict

Best for Training

H100 for large-scale training where time-to-completion matters. A100 80GB for budget-conscious training and fine-tuning.

Best for Inference

A100 80GB often wins on cost-per-query. H100 wins when latency is critical.

Best Value

A100 80GB typically delivers better TFLOPS per dollar, making it the value choice for cost-sensitive workloads.

Frequently Asked Questions

Is the H100 3x faster than the A100?

In raw FP16 TFLOPS, yes — the H100 delivers 990 vs 312 TFLOPS (3.2x). Real-world speedups depend on the workload but typically range from 2-3x for training and 1.5-2.5x for inference.

Should I upgrade from A100 to H100?

If training time is your bottleneck and you're training large models (10B+ parameters), the H100's performance advantage justifies the cost premium. For smaller models or inference workloads, the A100 may still offer better value.