Kimi K2.5/K2.6/K2.7-Code 1T — H200 vs MI300X Performance per Dollar
Cost per million tokens of H200 (NVIDIA Hopper) versus MI300X (AMD CDNA 3) on Kimi K2.5/K2.6/K2.7-Code 1T. 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: $0.75 per million tokens. MI300X: $3.31. Both at 37 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T, with H200 344% cheaper.
Around the middle of the 34–48 tok/s/user interactivity band — at 41 tok/s/user — H200 runs $0.85 per million tokens on Kimi K2.5/K2.6/K2.7-Code 1T while MI300X runs $3.94. H200 is the cheaper choice by 366%.
On Kimi K2.5/K2.6/K2.7-Code 1T at 45 tok/s/user, the per-million math comes out to $0.94 for H200 and $4.72 for MI300X; H200 delivers 401% more output per dollar. (Numbers reflect the default 1k/1k · int4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
Chip pricing (owning hyperscaler): H200 $1.22/chip/hr · MI300X $0.95/chip/hr. Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model.
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | H200:$0.746MI300X:$3.311 | H200:$0.846MI300X:$3.941 | H200:$0.942MI300X:$4.721 |
| Concurrency | H200:~53MI300X:~9 | H200:~42MI300X:~7 | H200:~33MI300X:~5 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.