NVIDIA Datacenter GPUs: H100 vs A100
NVIDIA H100 (Hopper Architecture) The H100 is NVIDIA's flagship AI accelerator, designed specifically for large language models and generative AI workloads.
H100 Specifications
| Spec | H100 SXM | H100 PCIe |
|---|---|---|
| Memory | 80GB HBM3 | 80GB HBM3 |
| Bandwidth | 3.35 TB/s | 2.0 TB/s |
| TDP | 700W | 350W |
| Tensor TFLOPs (FP8) | 3,958 | 1,979 |
| NVLink | 900 GB/s | 600 GB/s |
Key H100 Features
- Transformer Engine: Dynamic FP8/FP16 precision switching
- 2nd Gen MIG: Up to 7 isolated instances per GPU
- NVLink 4.0: 18 links for multi-GPU scaling
NVIDIA A100 (Ampere Architecture) The A100 remains widely deployed and cost-effective for many workloads.
A100 Specifications
| Spec | A100 80GB | A100 40GB |
|---|---|---|
| Memory | 80GB HBM2e | 40GB HBM2e |
| Bandwidth | 2.0 TB/s | 1.6 TB/s |
| TDP | 400W | 400W |
| Tensor TFLOPs (TF32) | 312 | 312 |
Performance Comparison
- H100 is approximately 3x faster than A100 for LLM inference
- For training, H100 offers 2-4x speedup depending on workload
- A100 still excellent value for many production workloads
Use Cases
- H100: Large LLM training, real-time inference requiring lowest latency
- A100: Cost-effective inference, smaller model training, batch processing
h100a100datacenter gpu
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