DeepSeek V4 Pro 1.6T — H200 vs MI300X Performance per Dollar
Cost per million tokens of H200 (NVIDIA Hopper) versus MI300X (AMD CDNA 3) on DeepSeek V4 Pro 1.6T. 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 DeepSeek V4 Pro 1.6T at 13 tok/s/user, the per-million math comes out to $0.92 for H200 and $1.01 for MI300X; H200 delivers 10% more output per dollar.
At 19 tok/s/user on DeepSeek V4 Pro 1.6T, H200 costs $0.98 per million tokens; MI300X costs $2.03. H200 is 106% more cost-efficient at this operating point.
H200 edges MI300X at 26 tok/s/user on DeepSeek V4 Pro 1.6T — $1.09 per million tokens versus $3.26, a 199% cost-per-token gap. (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:$0.915MI300X:$1.005 | H200:$0.985MI300X:$2.030 | H200:$1.089MI300X:$3.255 |
| Concurrency | H200:~117MI300X:~84 | H200:~79MI300X:~29 | H200:~52MI300X:~13 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.