Qwen 3.5 397B-A17B — H200 vs MI300X Performance per Dollar
Cost per million tokens of H200 (NVIDIA Hopper) versus MI300X (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.
H200 edges MI300X at 43 tok/s/user on Qwen 3.5 397B-A17B — $0.54 per million tokens versus $0.76, a 41% cost-per-token gap.
Push Qwen 3.5 397B-A17B to 53 tok/s/user and H200 lands at $0.60 per million tokens against MI300X's $1.38 — H200 pulls ahead by 131%.
H200: $0.65 per million tokens. MI300X: $2.13. Both at 62 tok/s/user on Qwen 3.5 397B-A17B, with H200 228% cheaper. (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): H200 $1.22/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 | H200:$0.538MI300X:$0.758 | H200:$0.600MI300X:$1.385 | H200:$0.651MI300X:$2.135 |
| Concurrency | H200:~58MI300X:~34 | H200:~42MI300X:~15 | H200:~34MI300X:~8 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.