Side-by-side comparison of NVIDIA H100 SXM5 80GB vs H200 SXM5 141GB for serving Llama 3.3 70B (FP8). See exactly how context length, VRAM, and bandwidth affect your deployment capacity — with transparent formulas, not marketing claims.
| Spec | H100 SXM5 | H200 SXM5 | Winner |
|---|---|---|---|
| VRAM | 80 GB HBM3 | 141 GB HBM3e | H200 |
| Memory bandwidth | 3,350 GB/s | 4,800 GB/s | H200 |
| FP16/BF16 dense TFLOPS | 990 TF | 990 TF | Tie |
| FP8 support | Native | Native | Tie |
| NVLink bandwidth | 900 GB/s | 900 GB/s | Tie |
| Typical cloud price | ~$2.50/hr | ~$4.00/hr | H100 |
| Release year | 2022 | 2024 | H200 |
| TDP (power) | 700W | 700W | Tie |
H200 doubles H100 concurrency at every context length due to 76% more VRAM (141 vs 80 GB). The free VRAM after loading model weights is the bottleneck — H200 has 70 GB free vs H100's 9 GB for Llama 70B FP8.
H100 can't serve 32K+ context for Llama 70B FP8 on a single GPU — the KV cache alone needs 10+ GB, exceeding the 9 GB free after loading 70.6 GB of weights. H200 handles 32K with room for 6 concurrent users.
H200 is 43% faster per-token (4,800 vs 3,350 GB/s HBM bandwidth) — decode throughput scales linearly with memory bandwidth for memory-bound LLM inference.
H100 is 60% cheaper per GPU-hour (~$2.50 vs ~$4.00) but the cost-per-token is often LOWER on H200 because the higher throughput more than compensates for the higher hourly price.
For context lengths under 8K, H100 is sufficient and cheaper. For 8K-32K, H200 is strongly preferred (H100 can't fit 32K on a single GPU). For 128K+, H200 is the minimum — H100 can't fit it even with TP×2.
H200 is ~43% faster per-token decode throughput (4,800 vs 3,350 GB/s HBM bandwidth). For aggregate batched throughput, H200 is 2-8× better than H100 at long context because it has 7× more free VRAM for KV cache.
At 32K+ context, yes — H100 can't even fit the workload, so H200 is the only option. At short context (4K), H100 may be more cost-effective. Use the calculator above to find your break-even point.
No — a single H100 (80 GB) can't fit Llama 70B FP8 (70.6 GB weights) + 128K KV cache (~42 GB) = 112 GB total. You'd need at least 2× H100 (160 GB) or 1× H200 (141 GB).
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