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MiniMax M2.5/M2.7 · Chip comparison

MiniMax M2.5/M2.7 — GB300 NVL72 vs MI300X

Head-to-head AI inference benchmark comparison of GB300 NVL72 (NVIDIA Blackwell) and MI300X (AMD CDNA 3) 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.

Setting 39 tok/s/user as the target on MiniMax M2.5/M2.7, GB300 NVL72 produces 12484 tok/s/chip ($0.05 per million tokens) and MI300X produces 1517 ($0.17). GB300 NVL72 is 238% cheaper per token; GB300 NVL72 delivers 723% more tok/s/chip.

At 54 tok/s/user interactivity on MiniMax M2.5/M2.7, GB300 NVL72 delivers 8398 tok/s/chip at $0.08 per million tokens; MI300X delivers 1311 tok/s/chip at $0.20. GB300 NVL72 is 163% cheaper per token; GB300 NVL72 delivers 541% more tok/s/chip at this point.

GB300 NVL72 posts 4972 tok/s/chip for $0.13 per million tokens at 69 tok/s/user on MiniMax M2.5/M2.7; MI300X posts 1028 tok/s/chip for $0.26. GB300 NVL72 is 99% cheaper per token; GB300 NVL72 delivers 383% 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 NVL72:12484.2MI300X:1517.0
GB300 NVL72:8398.4MI300X:1310.9
GB300 NVL72:4972.4MI300X:1028.5
Cost ($/M tok)
GB300 NVL72:$0.051MI300X:$0.174
GB300 NVL72:$0.076MI300X:$0.201
GB300 NVL72:$0.129MI300X:$0.257
tok/s/MW
GB300 NVL72:5888764MI300X:1091393
GB300 NVL72:3961488MI300X:943119
GB300 NVL72:2345469MI300X:739905
Concurrency
GB300 NVL72:~613MI300X:~10
GB300 NVL72:~222MI300X:~5
GB300 NVL72:~63MI300X:~4

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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