Kimi K2.5/K2.6/K2.7-Code 1T · Performance per Dollar

Kimi K2.5/K2.6/K2.7-Code 1T — GB300 NVL72 vs MI325X Performance per Dollar

Cost per million tokens of GB300 NVL72 (NVIDIA Blackwell) versus MI325X (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.

GB300 NVL72 costs $0.14 per million tokens at 50 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T; we have no MI325X benchmark data at this exact target.

At 85 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T, GB300 NVL72 comes in at $1.52 per million tokens. MI325X hasn't been benchmarked at this operating point.

Only GB300 NVL72 has cost data at 119 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T — $3.14 per million tokens. MI325X is unmeasured at this target. (Numbers reflect the default 1k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)

GPU pricing (owning hyperscaler): GB300 NVL72 $2.65/GPU/hr · MI325X $1.28/GPU/hr. Source: SemiAnalysis Market August 2025 Pricing Surveys & AI Cloud TCO Model.

View full latency + throughput comparison →

Kimi K2.5/K2.6/K2.7-Code 1T: GB300 NVL72 versus MI325X cost per million tokens at matched interactivity levels
GB300 NVL72 versus MI325X cost per million tokens for this comparison's canonical default workload. Lower cost indicates better performance per dollar.
Interpolated from real benchmark data. Edit target interactivity values below to compare at different operating points.
Metric
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Dollar per Million Tokens
GB300 NVL72:$0.138MI325X:
GB300 NVL72:$1.524MI325X:
GB300 NVL72:$3.138MI325X:
Concurrency
GB300 NVL72:~1324MI325X:
GB300 NVL72:~95MI325X:
GB300 NVL72:~34MI325X:

Inference Performance

Inference performance metrics across different models, hardware configurations, and serving parameters.