Head-to-head AI inference benchmark comparison of GB300 NVL72 (NVIDIA Blackwell) and H100 (NVIDIA Hopper) on MiniMax M2.5/M2.7. 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.
GB300 NVL72 posts 10654 tok/s/chip for $0.06 per million tokens at 43 tok/s/user on MiniMax M2.5/M2.7; H100 posts 1926 tok/s/chip for $0.17. GB300 NVL72 is 180% cheaper per token; GB300 NVL72 delivers 453% more tok/s/chip.
Throughput at 62 tok/s/user on MiniMax M2.5/M2.7: GB300 NVL72 hits 7309 tok/s/chip, H100 hits 1363. Per-million costs land at $0.09 and $0.24 respectively. GB300 NVL72 is 172% cheaper per token; GB300 NVL72 delivers 436% more tok/s/chip.
GB300 NVL72 / H100 on MiniMax M2.5/M2.7 at 81 tok/s/user: 3698 / 950 tok/s/chip, $0.17 / $0.34 per million tokens. GB300 NVL72 is 97% cheaper per token; GB300 NVL72 delivers 289% more tok/s/chip. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB300 NVL72:10653.9H100:1926.3 | GB300 NVL72:7308.7H100:1363.2 | GB300 NVL72:3698.0H100:950.1 |
| Cost ($/M tok) | GB300 NVL72:$0.060H100:$0.169 | GB300 NVL72:$0.088H100:$0.238 | GB300 NVL72:$0.174H100:$0.342 |
| tok/s/MW | GB300 NVL72:5025439H100:1406025 | GB300 NVL72:3447521H100:995043 | GB300 NVL72:1744352H100:693508 |
| Concurrency | GB300 NVL72:~200H100:~41 | GB300 NVL72:~197H100:~20 | GB300 NVL72:~51H100:~11 |
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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MiniMax M2.5/2.7 230B 8K / 1K Total Tokens per $1 TCO vs. Interactivity