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MiniMax M2.5/M2.7 · Chip comparison

MiniMax M2.5/M2.7 — B300 vs GB300 NVL72

Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and GB300 NVL72 (NVIDIA Blackwell) 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.

B300 posts 13188 tok/s/chip for $0.05 per million tokens at 62 tok/s/user on MiniMax M2.5/M2.7; GB300 NVL72 posts 16544 tok/s/chip for $0.04. GB300 NVL72 is 23% cheaper per token; GB300 NVL72 delivers 25% more tok/s/chip.

Throughput at 101 tok/s/user on MiniMax M2.5/M2.7: B300 hits 6697 tok/s/chip, GB300 NVL72 hits 8079. Per-million costs land at $0.09 and $0.08 respectively. GB300 NVL72 is 18% cheaper per token; GB300 NVL72 delivers 21% more tok/s/chip.

B300 / GB300 NVL72 on MiniMax M2.5/M2.7 at 139 tok/s/user: 3406 / 3804 tok/s/chip, $0.18 / $0.17 per million tokens. GB300 NVL72 is 9% cheaper per token; GB300 NVL72 delivers 12% more tok/s/chip. (Numbers reflect the default 8k/1k · fp4 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)
B300:13188.2GB300 NVL72:16544.4
B300:6697.5GB300 NVL72:8078.6
B300:3405.8GB300 NVL72:3804.3
Cost ($/M tok)
B300:$0.048GB300 NVL72:$0.039
B300:$0.094GB300 NVL72:$0.079
B300:$0.184GB300 NVL72:$0.169
tok/s/MW
B300:6941146GB300 NVL72:7803974
B300:3524994GB300 NVL72:3810678
B300:1792521GB300 NVL72:1794491
Concurrency
B300:~262GB300 NVL72:~340
B300:~9GB300 NVL72:~48
B300:~15GB300 NVL72:~17

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.

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