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Llama 3.3 70B · Chip comparison

Llama 3.3 70B — B200 vs H100

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.)

View performance-per-dollar view →

Interpolated from real benchmark data. Edit target interactivity values below to compare at different operating points.
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

Inference Performance

Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.

Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity

Llama 3.3 70B Instruct • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™

TCO $/chip/hr: VR200: 3.61H100: 1.55H200: 1.59B200: 2.07B300: 2.52GB200: 2.26GB300: 2.79MI300X: 1.16MI325X: 1.32MI355X: 2.09RTX6000PRO: 0.75Jalapeño (Teacup): 1.56

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.

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