Head-to-head AI inference benchmark comparison of MI300X (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 β same interactions as the main inference chart.
MI300X / MI355X on MiniMax M2.5/M2.7 at 27 tok/s/user: 1598 / 6602 tok/s/chip, $0.17 / $0.06 per million tokens. MI355X is 162% cheaper per token; MI355X delivers 313% more tok/s/chip.
Around the middle of the 8β84 tok/s/user interactivity band, at 46 tok/s/user on MiniMax M2.5/M2.7: MI300X runs 1442 tok/s/chip at $0.18/M tokens, MI355X runs 4394 at $0.09/M. MI355X is 93% cheaper per token; MI355X delivers 205% more tok/s/chip.
Setting 65 tok/s/user as the target on MiniMax M2.5/M2.7, MI300X produces 1104 tok/s/chip ($0.24 per million tokens) and MI355X produces 2971 ($0.14). MI355X is 70% cheaper per token; MI355X delivers 169% more tok/s/chip. (Numbers reflect the default 8k/1k Β· fp8 selection for this URL β table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | MI300X:1597.6MI355X:6602.3 | MI300X:1441.8MI355X:4394.5 | MI300X:1104.4MI355X:2970.6 |
| Cost ($/M tok) | MI300X:$0.165MI355X:$0.063 | MI300X:$0.183MI355X:$0.095 | MI300X:$0.239MI355X:$0.140 |
| tok/s/MW | MI300X:1149340MI355X:3158973 | MI300X:1037258MI355X:2102615 | MI300X:794503MI355X:1421350 |
| Concurrency | MI300X:~25MI355X:~124 | MI300X:~7MI355X:~33 | MI300X:~4MI355X:~10 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr:
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