Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and H100 (NVIDIA Hopper) on Llama 3.3 70B. Latency, throughput, and cost across LLM workloads. Use the chart controls below to switch sequences, precisions, and metrics — same interactions as the main inference chart.
Throughput at 38 tok/s/user on Llama 3.3 70B: B200 hits 6519 tok/s/chip, H100 hits 1848. Per-million costs land at $0.07 and $0.18 respectively. B200 is 139% cheaper per token; B200 delivers 253% more tok/s/chip.
B200 / H100 on Llama 3.3 70B at 58 tok/s/user: 5052 / 1171 tok/s/chip, $0.10 / $0.28 per million tokens. B200 is 192% cheaper per token; B200 delivers 331% more tok/s/chip.
Toward the upper edge of the 19–97 tok/s/user interactivity band, at 78 tok/s/user on Llama 3.3 70B: B200 runs 3880 tok/s/chip at $0.12/M tokens, H100 runs 877 at $0.37/M. B200 is 199% cheaper per token; B200 delivers 342% more tok/s/chip. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B200:6519.0H100:1847.6 | B200:5051.6H100:1171.4 | B200:3879.9H100:877.4 |
| Cost ($/M tok) | B200:$0.074H100:$0.176 | B200:$0.095H100:$0.277 | B200:$0.124H100:$0.370 |
| tok/s/MW | B200:3812307H100:1348638 | B200:2954131H100:855073 | B200:2268961H100:640410 |
| Concurrency | B200:~41H100:~20 | B200:~32H100:~16 | B200:~27H100:~8 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr:
Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate cost per million tokens per decode chip or per prefill chip, rather than per total chip count. This makes direct token cost comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate input throughput per decode chip or per prefill chip, rather than per total chip count. This makes direct input throughput comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate output throughput per decode chip or per prefill chip, rather than per total chip count. This makes direct output throughput comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate power per decode chip or per prefill chip, rather than per total chip count. This makes direct power comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate Joules per decode chip or per prefill chip, rather than per total chip count. This makes direct Joules per token comparison with aggregated configs not an apples-to-apples comparison.
Matching measurements exist, but their chip series are hidden.
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Llama 3.3 70B Instruct 8K / 1K Total Tokens per $1 TCO vs. Interactivity