Kimi K2.5/K2.6/K2.7-Code 1T — H200 vs MI355X Performance per Dollar
Cost per million tokens of H200 (NVIDIA Hopper) versus MI355X (AMD CDNA 4) 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.
MI355X edges H200 at 46 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T — $0.96 per million tokens versus $1.12, a 16% cost-per-token gap.
Push Kimi K2.5/K2.6/K2.7-Code 1T to 59 tok/s/user and H200 lands at $1.42 per million tokens against MI355X's $1.37 — MI355X pulls ahead by 4%.
H200: $1.79 per million tokens. MI355X: $1.88. Both at 72 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T, with H200 5% cheaper. (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.)
GPU pricing (owning hyperscaler): H200 $1.41/GPU/hr · MI355X $1.48/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:$1.116MI355X:$0.961 | H200:$1.421MI355X:$1.371 | H200:$1.795MI355X:$1.879 |
| Concurrency | H200:~31MI355X:~19 | H200:~19MI355X:~10 | H200:~12MI355X:~6 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.