Qwen 3.5 397B-A17B — GB300 NVL72 vs MI325X Performance per Dollar
Cost per million tokens of GB300 NVL72 (NVIDIA Blackwell) versus MI325X (AMD CDNA 3) on Qwen 3.5 397B-A17B. 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 edges MI325X at 47 tok/s/user on Qwen 3.5 397B-A17B — $0.06 per million tokens versus $0.44, a 694% cost-per-token gap.
Push Qwen 3.5 397B-A17B to 54 tok/s/user and GB300 NVL72 lands at $0.06 per million tokens against MI325X's $0.58 — GB300 NVL72 pulls ahead by 936%.
GB300 NVL72: $0.06 per million tokens. MI325X: $0.84. Both at 61 tok/s/user on Qwen 3.5 397B-A17B, with GB300 NVL72 1334% cheaper. (Numbers reflect the default 8k/1k · fp8 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.
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | GB300 NVL72:$0.056MI325X:$0.445 | GB300 NVL72:$0.056MI325X:$0.580 | GB300 NVL72:$0.059MI325X:$0.840 |
| Concurrency | GB300 NVL72:~2304MI325X:~16 | GB300 NVL72:~2304MI325X:~11 | GB300 NVL72:~1635MI325X:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.