Head-to-head AI inference benchmark comparison of H200 (NVIDIA Hopper) and MI300X (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 11–84 tok/s/user interactivity band, at 29 tok/s/user on MiniMax M2.5/M2.7: H200 runs 3752 tok/s/chip at $0.09/M tokens, MI300X runs 1578 at $0.17/M. H200 is 85% cheaper per token; H200 delivers 138% more tok/s/chip.
Setting 48 tok/s/user as the target on MiniMax M2.5/M2.7, H200 produces 2595 tok/s/chip ($0.13 per million tokens) and MI300X produces 1412 ($0.19). H200 is 43% cheaper per token; H200 delivers 84% more tok/s/chip.
At 66 tok/s/user interactivity on MiniMax M2.5/M2.7, H200 delivers 2038 tok/s/chip at $0.17 per million tokens; MI300X delivers 1086 tok/s/chip at $0.24. H200 is 46% cheaper per token; H200 delivers 88% more tok/s/chip at this point. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | H200:3751.6MI300X:1578.3 | H200:2594.8MI300X:1412.0 | H200:2037.5MI300X:1085.6 |
| Cost ($/M tok) | H200:$0.090MI300X:$0.167 | H200:$0.131MI300X:$0.187 | H200:$0.166MI300X:$0.243 |
| tok/s/MW | H200:2738410MI300X:1135484 | H200:1893989MI300X:1015827 | H200:1487257MI300X:781024 |
| Concurrency | H200:~58MI300X:~21 | H200:~25MI300X:~7 | H200:~14MI300X:~4 |
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.
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MiniMax M2.5/2.7 230B 8K / 1K Total Tokens per $1 TCO vs. Interactivity