Head-to-head AI inference benchmark comparison of MI325X (AMD CDNA 3) 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.
Near the low end of the 13–99 tok/s/user interactivity band, at 34 tok/s/user on MiniMax M2.5/M2.7: MI325X runs 2699 tok/s/chip at $0.11/M tokens, MI355X runs 5707 at $0.07/M. MI355X is 55% cheaper per token; MI355X delivers 111% more tok/s/chip.
Setting 56 tok/s/user as the target on MiniMax M2.5/M2.7, MI325X produces 1814 tok/s/chip ($0.17 per million tokens) and MI355X produces 3529 ($0.12). MI355X is 43% cheaper per token; MI355X delivers 94% more tok/s/chip.
At 78 tok/s/user interactivity on MiniMax M2.5/M2.7, MI325X delivers 1009 tok/s/chip at $0.30 per million tokens; MI355X delivers 2312 tok/s/chip at $0.18. MI355X is 68% cheaper per token; MI355X delivers 129% more tok/s/chip at this point. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | MI325X:2698.8MI355X:5706.9 | MI325X:1814.3MI355X:3528.6 | MI325X:1009.1MI355X:2311.6 |
| Cost ($/M tok) | MI325X:$0.113MI355X:$0.073 | MI325X:$0.168MI355X:$0.118 | MI325X:$0.303MI355X:$0.180 |
| tok/s/MW | MI325X:1596901MI355X:2730570 | MI325X:1073566MI355X:1688334 | MI325X:597118MI355X:1106051 |
| Concurrency | MI325X:~18MI355X:~84 | MI325X:~8MI355X:~14 | MI325X:~5MI355X:~7 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr: VR200: 3.61H100: 1.17H200: 1.22B200: 1.73B300: 2.26GB200: 1.86GB300: 2.31MI300X: 0.95MI325X: 1.1MI355X: 1.5RTX6000PRO: 0.68Jalapeño (Teacup): 1.47
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.
Matching measurements exist, but their chip series are hidden.
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1 The ATOM engine is promising, however it has yet to serve production tokens. It is still in its infant stage.
MiniMax M2.5/2.7 230B 8K / 1K Total Tokens per $1 TCO vs. Interactivity