InferenceX
中文
MiniMax M2.5/M2.7 · Chip comparison

MiniMax M2.5/M2.7 — GB300 NVL72 vs H200

Head-to-head AI inference benchmark comparison of GB300 NVL72 (NVIDIA Blackwell) and H200 (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.

Throughput at 43 tok/s/user on MiniMax M2.5/M2.7: GB300 NVL72 hits 10654 tok/s/chip, H200 hits 2816. Per-million costs land at $0.06 and $0.12 respectively. GB300 NVL72 is 100% cheaper per token; GB300 NVL72 delivers 278% more tok/s/chip.

GB300 NVL72 / H200 on MiniMax M2.5/M2.7 at 62 tok/s/user: 7309 / 2166 tok/s/chip, $0.09 / $0.16 per million tokens. GB300 NVL72 is 78% cheaper per token; GB300 NVL72 delivers 237% more tok/s/chip.

Toward the upper edge of the 24–100 tok/s/user interactivity band, at 81 tok/s/user on MiniMax M2.5/M2.7: GB300 NVL72 runs 3698 tok/s/chip at $0.17/M tokens, H200 runs 1595 at $0.21/M. GB300 NVL72 is 22% cheaper per token; GB300 NVL72 delivers 132% 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)
GB300 NVL72:10653.9H200:2816.4
GB300 NVL72:7308.7H200:2165.7
GB300 NVL72:3698.0H200:1594.8
Cost ($/M tok)
GB300 NVL72:$0.060H200:$0.120
GB300 NVL72:$0.088H200:$0.156
GB300 NVL72:$0.174H200:$0.213
tok/s/MW
GB300 NVL72:5025439H200:2055797
GB300 NVL72:3447521H200:1580777
GB300 NVL72:1744352H200:1164064
Concurrency
GB300 NVL72:~200H200:~30
GB300 NVL72:~197H200:~16
GB300 NVL72:~51H200:~9

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

MiniMax M2.5/2.7 230B • 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.

No data available

Please change the model, sequence, precision, date range or chip selection.

Shift+Scroll to zoom • Drag to pan • Double-click to reset • Click a point to pin tooltip