When to Choose Each Option
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- Choose NVIDIA Blackwell (B200) when...
- Choose NVIDIA Hopper (H100) when...
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| Factor | NVIDIA Blackwell (B200) | NVIDIA Hopper (H100) |
|---|---|---|
| Inference Performance | 30x higher inference throughput via FP4, 192GB HBM3e, and NVLink Switch. Holds 1T-parameter models entirely in VRAM. Winner | Good inference performance in FP8 and FP16. Production deployment standard 2023-2025. Proven and broadly available. |
| Training Performance | 2-4x better than H100 for modern architectures via second-gen Transformer Engine. Winner | Market standard for LLM training 2022-2025. Well-suited for models up to 70B parameters on single node. |
| Energy Efficiency | Significantly more efficient: FP4 enables 4x more operations per watt. Winner | Good efficiency for its generation, but significantly less efficient than Blackwell. |
| Price and Availability | More expensive (B200: >$30k), limited availability in 2025. Long-term cheaper through efficiency gains. | Lower spot-market prices due to larger supply. H100: $25-28k, broad cloud availability. Winner |
| FP4 Support | Native FP4 at hardware level. Enables 2x more efficiency than FP8 without quality loss. Winner | No native FP4. FP8 as lowest precision tier. INT4 possible but with latency overhead. |
| Memory Capacity | 192GB HBM3e per B200 chip. GB200 NVL72 = 1.4TB total. Enables 400B+ parameter models without model parallelism. Winner | 80GB HBM3 per H100. DGX H100 = 640GB total. Sufficient for models up to ~70B without parallelism. |
| Total Score · 0 ties | 5 / 6 | 1 / 6 |
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