MiniMax M2.5/M2.7 — H200 vs MI300X Performance per Dollar
Cost per million tokens of H200 (NVIDIA Hopper) versus MI300X (AMD CDNA 3) on MiniMax M2.5/M2.7. Owning-hyperscaler TCO normalized by output tokens — performance per dollar across LLM workloads. Pick the more cost-efficient SKU at every target interactivity level. Use the chart controls below to switch sequences, precisions, and metrics — same interactions as the main inference chart.
H200: $0.22 per million tokens. MI300X: $0.25. Both at 44 tok/s/user on MiniMax M2.5/M2.7, with H200 12% cheaper.
Around the middle of the 27–95 tok/s/user interactivity band — at 61 tok/s/user — H200 runs $0.45 per million tokens on MiniMax M2.5/M2.7 while MI300X runs $0.40. MI300X is the cheaper choice by 12%.
On MiniMax M2.5/M2.7 at 78 tok/s/user, the per-million math comes out to $0.82 for H200 and $0.75 for MI300X; MI300X delivers 9% more output per dollar. (Numbers reflect the default 1k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
GPU pricing (owning hyperscaler): H200 $1.41/GPU/hr · MI300X $1.12/GPU/hr. Source: SemiAnalysis Market August 2025 Pricing Surveys & AI Cloud TCO Model.
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | H200:$0.223MI300X:$0.250 | H200:$0.452MI300X:$0.402 | H200:$0.816MI300X:$0.747 |
| Concurrency | H200:~80MI300X:~63 | H200:~63MI300X:~26 | H200:~21MI300X:~11 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.