The NVIDIA H100 Tensor Core GPU is the current gold standard for Generative AI, promising up to 9x faster training than the A100. But with a price tag nearly triple that of its predecessor, the question for every CTO is simple: Is the speed worth the cost?
Benchmark Setup
We ran a controlled experiment fine-tuning Llama-3-70b-Instruct on a proprietary financial dataset comprising 500,000 documents (approx. 200GB text).
- Cluster A: 8x NVIDIA A100 (80GB) SXM4
- Cluster B: 8x NVIDIA H100 (80GB) SXM5
- Precision: BF16 (A100) vs FP8 (H100)
Performance Results
| Metric | A100 Cluster | H100 Cluster | Delta |
|---|---|---|---|
| Training Time (Epoch) | 42 Hours | 14 Hours | 3x Faster |
| Throughput (Tok/s) | 18,000 | 52,000 | 2.8x Higher |
| VRAM Efficiency (FP8) | N/A | +40% Batch Size | Transformer Engine |
The FP8 Advantage
The secret weapon of the H100 is the Transformer Engine, which dynamically casts layers to FP8 precision without losing model accuracy. This effectively doubles the memory bandwidth and compute throughput compared to the BF16 standard used on A100s.
Cost Analysis (CapEx vs OpEx)
While the H100 hardware is ~3x more expensive, the 3x reduction in training time means that for heavy workloads (constant retraining), the H100 actually offers a lower Total Cost of Ownership (TCO) over a 3-year period due to energy savings and faster iteration cycles.
"For sporadic inference, the A100 is still a workhorse. For continuous training and high-throughput RAG, the H100 pays for itself in 6 months."