The Battle of the Titans: H100 vs A100
NVIDIA's H100 and A100 are the workhorses of modern AI infrastructure. But which one should you choose for your workload? In this guide, we'll compare real-world benchmarks across LLM training, inference, and fine-tuning.
The short answer: H100 is 2-3x faster for most AI workloads, but costs more. The A100 offers better price-performance for many use cases.
Hardware Specifications
| Specification | H100 SXM | A100 80GB |
|---|---|---|
| Architecture | Hopper | Ampere |
| VRAM | 80 GB HBM3 | 80 GB HBM2e |
| Memory Bandwidth | 3.35 TB/s | 2.0 TB/s |
| FP16 TFLOPS | 1,979 | 624 |
| FP8 TFLOPS | 3,958 | N/A |
| TDP | 700W | 400W |
| NVLink Bandwidth | 900 GB/s | 600 GB/s |
| Transformer Engine | Yes (4th Gen) | No |
The H100's key advantages are its Transformer Engine (optimized for attention mechanisms) and FP8 support (enabling faster training with minimal accuracy loss).
LLM Training Benchmarks
We tested training throughput on Llama-style architectures across different model sizes:
| Model Size | H100 (tokens/sec) | A100 (tokens/sec) | H100 Speedup |
|---|---|---|---|
| 7B parameters | 12,400 | 5,200 | 2.4x |
| 13B parameters | 6,800 | 2,900 | 2.3x |
| 70B parameters | 1,850 | 720 | 2.6x |
Key finding: The H100's advantage grows with larger models due to better memory bandwidth and the Transformer Engine.
Inference Benchmarks
For LLM inference using vLLM, we measured tokens per second at various batch sizes:
| Workload | H100 | A100 | H100 Speedup |
|---|---|---|---|
| Llama 3 8B (batch 1) | 95 tok/s | 42 tok/s | 2.3x |
| Llama 3 8B (batch 32) | 2,400 tok/s | 980 tok/s | 2.4x |
| Llama 3 70B (batch 1) | 28 tok/s | 12 tok/s | 2.3x |
| Mixtral 8x7B (batch 8) | 680 tok/s | 290 tok/s | 2.3x |
Fine-Tuning Performance
Fine-tuning is where the H100 really shines, especially with techniques like LoRA and QLoRA:
| Task | H100 Time | A100 Time | H100 Speedup |
|---|---|---|---|
| LoRA fine-tune 7B (1 epoch) | 18 min | 42 min | 2.3x |
| Full fine-tune 7B (1 epoch) | 2.1 hrs | 5.8 hrs | 2.8x |
| QLoRA 70B (1 epoch) | 3.2 hrs | 8.5 hrs | 2.7x |
Cost-Performance Analysis
Here's where it gets interesting. On GPUBrazil:
| GPU | Price/Hour | Relative Performance | Cost per Unit Work |
|---|---|---|---|
| H100 80GB | $2.80 | 2.5x baseline | $1.12/unit |
| A100 80GB | $1.60 | 1.0x baseline | $1.60/unit |
| L40S | $0.90 | 0.6x baseline | $1.50/unit |
💡 The Verdict
H100 has the best cost-per-performance on GPUBrazil at current prices. You get 2.5x the performance for only 1.75x the price.
When to Choose Each GPU
Choose H100 if:
- Training large models (>13B parameters)
- Time-to-result matters more than cost
- Running production inference at scale
- Using FP8 quantization (H100-only feature)
- Working with attention-heavy architectures
Choose A100 if:
- Training smaller models (<13B parameters)
- Budget is the primary constraint
- Running experiments and prototyping
- Need maximum VRAM at lowest cost
Real-World Example: Training a 7B Model
Let's say you need to train a 7B parameter model for 100,000 steps:
| Metric | 8x H100 | 8x A100 |
|---|---|---|
| Time to complete | ~8 hours | ~19 hours |
| Hourly cost | $22.40 | $12.80 |
| Total cost | $179.20 | $243.20 |
The H100 is both faster AND cheaper for this workload because the time savings outweigh the higher hourly rate.
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The H100 is the clear winner for most AI workloads in 2025. Its ~2.5x performance advantage and excellent price-performance ratio on platforms like GPUBrazil make it the default choice.
The A100 remains relevant for budget-conscious projects, smaller models, and cases where raw VRAM matters more than compute speed.
The best part? With cloud GPUs, you don't have to commit. Sign up for GPUBrazil and test both to find what works best for your specific workload.