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MiniMax M2.5/M2.7 · Chip comparison

MiniMax M2.5/M2.7 — GB300 NVL72 vs MI325X

Head-to-head AI inference benchmark comparison of GB300 NVL72 (NVIDIA Blackwell) and MI325X (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.

Near the low end of the 24–99 tok/s/user interactivity band, at 43 tok/s/user on MiniMax M2.5/M2.7: GB300 NVL72 runs 10654 tok/s/chip at $0.06/M tokens, MI325X runs 2291 at $0.13/M. GB300 NVL72 is 121% cheaper per token; GB300 NVL72 delivers 365% more tok/s/chip.

Setting 62 tok/s/user as the target on MiniMax M2.5/M2.7, GB300 NVL72 produces 7309 tok/s/chip ($0.09 per million tokens) and MI325X produces 1584 ($0.19). GB300 NVL72 is 120% cheaper per token; GB300 NVL72 delivers 361% more tok/s/chip.

At 81 tok/s/user interactivity on MiniMax M2.5/M2.7, GB300 NVL72 delivers 3698 tok/s/chip at $0.17 per million tokens; MI325X delivers 910 tok/s/chip at $0.34. GB300 NVL72 is 93% cheaper per token; GB300 NVL72 delivers 306% more tok/s/chip at this point. (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 NVL72:10653.9MI325X:2290.7
GB300 NVL72:7308.7MI325X:1584.2
GB300 NVL72:3698.0MI325X:910.3
Cost ($/M tok)
GB300 NVL72:$0.060MI325X:$0.133
GB300 NVL72:$0.088MI325X:$0.193
GB300 NVL72:$0.174MI325X:$0.336
tok/s/MW
GB300 NVL72:5025439MI325X:1355458
GB300 NVL72:3447521MI325X:937407
GB300 NVL72:1744352MI325X:538619
Concurrency
GB300 NVL72:~200MI325X:~12
GB300 NVL72:~197MI325X:~6
GB300 NVL72:~51MI325X:~7

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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