MiniMax M3 428B — B300 vs MI325X Performance per Dollar
Cost per million tokens of B300 (NVIDIA Blackwell) versus MI325X (AMD CDNA 3) on MiniMax M3 428B. 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.
At 54 tok/s/user on MiniMax M3 428B, B300 costs $0.18 per million tokens; MI325X costs $0.58. B300 is 224% more cost-efficient at this operating point.
B300 edges MI325X at 97 tok/s/user on MiniMax M3 428B — $0.46 per million tokens versus $1.50, a 224% cost-per-token gap.
Push MiniMax M3 428B to 139 tok/s/user and B300 lands at $0.57 per million tokens against MI325X's $2.66 — B300 pulls ahead by 365%. (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.)
Chip pricing (owning hyperscaler): B300 $2.26/chip/hr · MI325X $1.10/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:$0.179MI325X:$0.581 | B300:$0.464MI325X:$1.504 | B300:$0.571MI325X:$2.657 |
| Concurrency | B300:~269MI325X:~43 | B300:~42MI325X:~9 | B300:~18MI325X:~4 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.