ARCHITECTURE COMPARISON
H200 uses Hopper architecture with HBM3e memory providing 141 GB at 4.8 TB/s bandwidth. B200 uses Blackwell with 192 GB HBM3e at 8 TB/s bandwidth, second-gen Transformer Engine with FP4 support, and 2.5x FP8 TFLOPS versus H200. The architectural differences translate to 1.4-2.5x real-world throughput depending on workload, quantization, and batch size.
THROUGHPUT BENCHMARKS
Llama 3 70B at FP8: H200 achieves 5,200 tok/s, B200 achieves 7,500 tok/s (1.44x). At NVFP4: B200 achieves 15,000 tok/s (2.88x H200 FP8). Llama 3 405B: H200 needs 8 GPUs for 6,500 tok/s, B200 needs 4 GPUs for 8,200 tok/s (1.26x throughput, 2x GPU efficiency). Long context 128K: H200 batch size limited to 4, B200 batch size reaches 12 due to 192 GB VRAM.
| Workload | H200 | B200 | Ratio |
|---|---|---|---|
| 70B FP8 4K | 5,200 tok/s | 7,500 tok/s | 1.44x |
| 70B FP4 4K | N/A | 15,000 tok/s | 2.88x |
| 405B FP8 4K | 6,500 tok/s | 8,200 tok/s | 1.26x |
| 70B FP8 128K | 1,800 tok/s | 4,200 tok/s | 2.33x |
| 70B cost/M tok | $0.17 | $0.17 | 1.0x |
| 70B FP4 cost/M tok | N/A | $0.08 | 2.1x |
COST-PER-TOKEN ANALYSIS
At comparable pricing ($3.20/hr H200 vs $4.50/hr B200), FP8 inference cost-per-token is nearly identical at $0.17/M tokens. B200's advantage unlocks at NVFP4: $0.08/M tokens, 2.1x improvement. For long context (128K), B200 achieves $0.10/M tokens versus H200's $0.25/M tokens due to batch size advantage. The cost-per-token crossover occurs at 60-70% utilization for both GPUs.
UPGRADE TIMING
In 2025, B200 commands a 40-80% premium over H200 ($4.50-5.50/hr vs $2.80-3.50/hr reserved). The premium is justified for: workloads using FP4 quantization, long-context serving above 32K tokens, and training runs under 7 days where 2x throughput saves wall clock time. By 2026, B200 premium narrows to 25-40% as Blackwell supply normalizes.
WORKLOAD-DEPENDENT VALUE
High-throughput FP8 inference: H200 wins on cost-per-token. FP4 inference: B200 wins at 2.1x better cost. Training runs above 7 days: B200 wins due to 1.4-1.8x throughput. Long context serving: B200 wins by 2-3x margin. Development and experimentation: H200 wins on lower cost-per-hour. Multi-tenant serving: B200 wins at scale due to 192 GB enabling larger batch sizes.
RECOMMENDATION
Do not upgrade from H200 to B200 for FP8 inference workloads-the cost-per-token is identical. Upgrade for FP4 inference (2.1x better), long context (2-3x better), or training throughput (1.4-1.8x better). Teams without H200 should choose based on workload: inference-first teams save with H200, training-heavy teams invest in B200. The 12-month TCO favors H200 for most inference workloads below 500M tokens/day.