MiniMax M2.5/M2.7 — H100 vs MI355X
Head-to-head AI inference benchmark comparison of H100 (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.
H100 posts 1926 tok/s/chip for $0.17 per million tokens at 43 tok/s/user on MiniMax M2.5/M2.7; MI355X posts 4697 tok/s/chip for $0.09. MI355X is 90% cheaper per token; MI355X delivers 144% more tok/s/chip.
Throughput at 66 tok/s/user on MiniMax M2.5/M2.7: H100 hits 1269 tok/s/chip, MI355X hits 2917. Per-million costs land at $0.26 and $0.14 respectively. MI355X is 79% cheaper per token; MI355X delivers 130% more tok/s/chip.
H100 / MI355X on MiniMax M2.5/M2.7 at 88 tok/s/user: 823 / 1869 tok/s/chip, $0.39 / $0.22 per million tokens. MI355X is 77% cheaper per token; MI355X delivers 127% more tok/s/chip. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | H100:1926.3MI355X:4697.0 | H100:1268.8MI355X:2917.5 | H100:823.2MI355X:1869.3 |
| Cost ($/M tok) | H100:$0.169MI355X:$0.089 | H100:$0.256MI355X:$0.143 | H100:$0.395MI355X:$0.223 |
| tok/s/MW | H100:1406025MI355X:2247388 | H100:926145MI355X:1395931 | H100:600902MI355X:894417 |
| Concurrency | H100:~41MI355X:~43 | H100:~18MI355X:~10 | H100:~9MI355X:~5 |
Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity
MiniMax M2.5/2.7 230B • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
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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Interactivity is the rate at which a single user receives generated tokens while the model streams its answer — how quickly new words appear on screen. Higher values feel snappier; operators trade it against batch throughput.
How many total tokens (input + output) one US dollar of infrastructure spend buys, priced with the all-in hourly ownership cost of a Neocloud Giant operator. It is the reciprocal of cost per token, so higher means cheaper.
Formula: tok/$ = (total tok/s/chip × 3,600) ÷ all-in cost per chip-hour ($)
1 The ATOM engine is promising, however it has yet to serve production tokens. It is still in its infant stage.