Choose H100 for cutting-edge LLM training (3x faster with Transformer Engine), large-scale distributed training requiring 80GB HBM3 memory, and production inference serving millions of requests. Choose A100 for cost-efficient training of models under 70B parameters, fine-tuning workloads, multi-GPU experiments, and general-purpose AI where 2x slower performance is acceptable for 40-50% cost savings. On io.net, H100 costs $2.20/hr vs. A100 at $1.20/hr (40GB) or $1.49/hr (80GB), making A100 the better value for most teams unless training cutting-edge 100B+ models.
H100 vs A100: Specs Comparison
| Specification | H100 SXM5 | A100 80GB SXM4 | A100 40GB SXM4 |
|---|---|---|---|
| Architecture | Hopper (2022) | Ampere (2020) | Ampere (2020) |
| Memory | 80GB HBM3 | 80GB HBM2e | 40GB HBM2e |
| Memory Bandwidth | 3.35 TB/s | 2.0 TB/s | 1.6 TB/s |
| FP16 Performance | 1,979 TFLOPS | 624 TFLOPS | 624 TFLOPS |
| Transformer Engine | Yes (FP8) | No | No |
| NVLink Bandwidth | 900 GB/s | 600 GB/s | 600 GB/s |
| TDP | 700W | 500W | 400W |
| io.net Price | $2.20/hr | $1.49/hr | $1.20/hr |
| AWS Price | $6.98/hr | $4.10/hr | $3.06/hr |
Performance Benchmarks: Real-World Training Speed
| Workload | H100 | A100 80GB | A100 40GB | H100 Speedup |
|---|---|---|---|---|
| Llama 3 8B Training (1 epoch) | 4.2 hours | 12.6 hours | 13.1 hours | 3.0x faster |
| Llama 3 70B Training (1 epoch) | 28 hours | 89 hours | N/A (OOM) | 3.2x faster |
| Stable Diffusion XL Fine-tuning | 2.1 hours | 5.8 hours | 6.2 hours | 2.8x faster |
| GPT-J 6B Inference (batch 32) | 1,200 tokens/sec | 580 tokens/sec | 550 tokens/sec | 2.1x faster |
| BERT Training (Large) | 3.5 hours | 9.2 hours | 9.8 hours | 2.6x faster |
Benchmarks measured on io.net infrastructure using PyTorch 2.3, CUDA 12.4, mixed precision training.
When to Choose H100
Best Use Cases:
- Frontier LLM training: GPT-4 scale, 100B+ parameter models requiring maximum throughput
- Large-scale distributed training: 8-256 GPU clusters where NVLink bandwidth is critical
- Production inference at scale: Serving 10M+ requests/day where 2x throughput = 50% cost reduction
- Research pushing SOTA: Cutting-edge architectures needing FP8 precision and Transformer Engine
- Time-critical projects: Launch deadlines where 3x faster training justifies 48% higher cost
Key H100 Advantages:
1. Transformer Engine (FP8 Precision):
H100's specialized Transformer Engine accelerates LLM training with FP8 precision, delivering 3x speedup on GPT/Llama architectures. A100 lacks this hardware, limiting it to FP16/BF16. For transformer-heavy workloads, this alone justifies H100's cost.
2. 80GB HBM3 Memory:
HBM3 offers 68% more bandwidth (3.35 TB/s vs. 2.0 TB/s) than A100's HBM2e. Critical for memory-bound workloads like inference serving with long context windows (32K+ tokens) or batch sizes above 32.
3. Superior Multi-GPU Scaling:
900 GB/s NVLink (vs. 600 GB/s on A100) improves gradient synchronization efficiency. On 8-GPU clusters, H100 achieves 95% scaling efficiency vs. 88% on A100, saving 8% of training time.
When to Choose A100
Best Use Cases:
- Fine-tuning pre-trained models: Llama 3 8B/13B, Stable Diffusion, Whisper adaptations
- Research experiments: Hyperparameter sweeps, ablation studies where 2-3x longer training is acceptable
- Cost-sensitive production: Inference serving under 1M requests/day where throughput isn't bottleneck
- Multi-GPU learning: Testing distributed training setups before scaling to H100 clusters
- General AI workloads: Computer vision, NLP, reinforcement learning not requiring cutting-edge speed
Key A100 Advantages:
1. 40-50% Lower Cost:
A100 80GB costs $1.49/hr on io.net vs. $2.20/hr for H100 (32% savings). A100 40GB at $1.20/hr saves 45%. For training jobs under 100 hours, this translates to $100-500 in savings per experiment.
2. Mature Ecosystem:
A100 has 4+ years of framework optimization (PyTorch, TensorFlow, JAX). More public benchmarks, tutorials, and community knowledge. H100 optimizations still emerging.
3. Wider Availability:
io.net has 3x more A100 inventory than H100. During high-demand periods, A100 availability remains 99%+ while H100 can dip to 95%.
4. 40GB Option for Cost Efficiency:
A100 40GB handles 90% of workloads at $1.20/hr (45% cheaper than H100). Only models above 30B parameters require 80GB memory.
Cost-Performance Analysis
Scenario: Training Llama 3 8B (Full Fine-tune):
| GPU | Training Time | io.net Cost/Hour | Total Cost | Cost per Hour Saved |
|---|---|---|---|---|
| H100 | 4.2 hours | $2.20 | $9.24 | - |
| A100 80GB | 12.6 hours | $1.49 | $18.77 | +$1.13/hr |
| A100 40GB | 13.1 hours | $1.20 | $15.72 | +$0.73/hr |
Winner: H100 — For single experiments, H100's speed offsets higher hourly cost. Total savings: $6.48-$9.53 per training run.
Scenario: Monthly Fine-Tuning (20 experiments):
| GPU | Total Hours | Total Cost | Savings vs. H100 |
|---|---|---|---|
| H100 | 84 hours | $184.80 | - |
| A100 80GB | 252 hours | $375.48 | -$190.68 |
| A100 40GB | 262 hours | $314.40 | -$129.60 |
Winner: H100 — Volume workloads favor faster GPUs. H100 saves $130-191/month vs. A100 despite higher hourly rate.
Scenario: Inference Serving (100K requests/day, 24/7):
| GPU | GPUs Needed | Monthly Cost (io.net) | Monthly Cost (AWS) |
|---|---|---|---|
| H100 | 2 | $3,168 | $10,053 |
| A100 80GB | 4 | $4,300 | $11,808 |
| A100 40GB | 5 | $4,320 | $11,016 |
Winner: H100 — Higher throughput reduces GPU count needed. H100 saves $1,132-1,152/month vs. A100 at scale.
Decision Framework
Choose H100 if:
- Training models > 70B parameters regularly
- Running > 10 training jobs/month (volume justifies speed premium)
- Production inference serving > 1M requests/day
- Budget allows 48% higher cost for 3x faster results
- Using FP8-optimized Transformer architectures (GPT, Llama, Falcon)
- Distributed training on 8+ GPU clusters
Choose A100 if:
- Fine-tuning pre-trained models under 30B parameters
- Running < 5 experiments/month (infrequent usage)
- Research/prototyping where speed isn't critical
- Budget-constrained (40-50% cost savings matter more than 3x speed)
- Learning distributed training before scaling to H100
- Inference serving under 500K requests/day
Try Both on io.net:
With per-second billing, run the same workload on H100 and A100 to measure actual cost difference. $100 free credits cover 45 hours of A100 or 83 hours of H100 testing.
Related Questions
Is the H100 worth 83% more cost than A100?
For high-volume training (20+ jobs/month) or production inference at scale, yes. H100's 3x speed reduces total cost despite higher hourly rate. For infrequent experimentation (<5 jobs/month), A100's lower cost wins. Run a TCO calculator on your specific workload to confirm.
Can I train 70B models on A100 40GB?
No. 70B models require 100-140GB VRAM (full precision) or 50-70GB (quantized). Use A100 80GB (single GPU with quantization) or 2x A100 80GB (distributed). Alternatively, use 8x A100 40GB cluster with model parallelism.
Does H100 support all the same frameworks as A100?
Yes. H100 runs all CUDA software targeting A100 (backward compatible). To unlock FP8 Transformer Engine, use PyTorch 2.1+, TensorFlow 2.13+, or JAX 0.4.13+ with Transformer Engine library. Older frameworks work but don't leverage H100's full speed.
How much faster is H100 for inference vs training?
H100 is 2.0-2.5x faster for inference (vs. 3.0x for training). Inference is more memory-bound than compute-bound, limiting H100's advantage. For cost-efficient inference, consider RTX 4090 ($0.18/hr, 70% of H100 throughput at 12x lower cost) unless you need 80GB memory.
Will there be a B100 GPU soon?
NVIDIA's Blackwell architecture (B100) is expected late 2026. Early benchmarks suggest 2-3x improvement over H100. On cloud platforms like io.net, you'll access B100s immediately upon release without purchasing new hardware. This is another advantage of cloud over on-premise.
Compare H100 and A100 Risk-Free
Don't guess — test both GPUs on io.net with real workloads:
- Per-second billing — pay only for actual usage
- H100: $2.20/hr vs. AWS $6.98/hr (68% savings)
- A100: $1.20-1.49/hr vs. AWS $3.06-4.10/hr (60-64% savings)
- Instant availability — both GPUs on-demand, no waitlists
Start comparing GPUs now → or view detailed benchmarks →
Last updated: May 2026 | Benchmarks based on PyTorch 2.3, CUDA 12.4, io.net infrastructure
