Kimi K2.5/K2.6/K2.7-Code 1T — B300 vs MI300X Performance per Dollar
Cost per million tokens of B300 (NVIDIA Blackwell) 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.
Push Kimi K2.5/K2.6/K2.7-Code 1T to 33 tok/s/user and B300 lands at $0.88 per million tokens against MI300X's $2.83 — B300 pulls ahead by 223%.
B300: $1.01 per million tokens. MI300X: $3.45. Both at 38 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T, with B300 241% cheaper.
Toward the upper edge of the 28–48 tok/s/user interactivity band — at 44 tok/s/user — B300 runs $1.16 per million tokens on Kimi K2.5/K2.6/K2.7-Code 1T while MI300X runs $4.52. B300 is the cheaper choice by 290%. (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): B300 $2.26/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 | B300:$0.879MI300X:$2.834 | B300:$1.013MI300X:$3.452 | B300:$1.159MI300X:$4.516 |
| Concurrency | B300:~47MI300X:~12 | B300:~34MI300X:~8 | B300:~23MI300X:~6 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.