Qwen 3.5 397B-A17B — MI300X vs MI325X Performance per Dollar
Cost per million tokens of MI300X (AMD CDNA 3) 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.
On Qwen 3.5 397B-A17B at 55 tok/s/user, the per-million math comes out to $0.84 for MI300X and $0.77 for MI325X; MI325X delivers 10% more output per dollar.
At 68 tok/s/user on Qwen 3.5 397B-A17B, MI300X costs $1.17 per million tokens; MI325X costs $1.08. MI325X is 8% more cost-efficient at this operating point.
MI325X edges MI300X at 82 tok/s/user on Qwen 3.5 397B-A17B — $1.66 per million tokens versus $1.75, a 6% cost-per-token gap. (Numbers reflect the default 1k/1k · bf16 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
Chip pricing (owning hyperscaler): MI300X $0.95/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 | MI300X:$0.842MI325X:$0.766 | MI300X:$1.166MI325X:$1.076 | MI300X:$1.749MI325X:$1.655 |
| Concurrency | MI300X:~23MI325X:~32 | MI300X:~13MI325X:~17 | MI300X:~8MI325X:~10 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.