MiniMax M2.5/M2.7 — H200 vs MI355X
Head-to-head AI inference benchmark comparison of H200 (NVIDIA Hopper) and MI355X (AMD CDNA 4) 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 36 tok/s/user on MiniMax M2.5/M2.7: H200 hits 3221 tok/s/chip, MI355X hits 5471. Per-million costs land at $0.11 and $0.08 respectively. MI355X is 38% cheaper per token; MI355X delivers 70% more tok/s/chip.
H200 / MI355X on MiniMax M2.5/M2.7 at 61 tok/s/user: 2197 / 3197 tok/s/chip, $0.15 / $0.13 per million tokens. MI355X is 18% cheaper per token; MI355X delivers 46% more tok/s/chip.
Toward the upper edge of the 11–110 tok/s/user interactivity band, at 86 tok/s/user on MiniMax M2.5/M2.7: H200 runs 1452 tok/s/chip at $0.23/M tokens, MI355X runs 1954 at $0.21/M. MI355X is 9% cheaper per token; MI355X delivers 35% more tok/s/chip. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | H200:3220.8MI355X:5471.2 | H200:2197.0MI355X:3197.3 | H200:1451.9MI355X:1953.9 |
| Cost ($/M tok) | H200:$0.105MI355X:$0.076 | H200:$0.154MI355X:$0.130 | H200:$0.233MI355X:$0.213 |
| tok/s/MW | H200:2350935MI355X:2617800 | H200:1603619MI355X:1529799 | H200:1059771MI355X:934901 |
| Concurrency | H200:~40MI355X:~72 | H200:~17MI355X:~12 | H200:~8MI355X:~5 |
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 - Hyperscaler) vs. Interactivity
MiniMax M2.5/2.7 230B • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
TCO $/chip/hr: H100: 1.17H200: 1.22B200: 1.73B300: 2.26GB200: 1.86GB300: 2.31MI300X: 0.95MI325X: 1.1MI355X: 1.5RTX6000PRO: 0.68
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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