DeepSeek V4 Pro 1.6T — B300 vs GB200 NVL72 Performance per Dollar
Cost per million tokens of B300 (NVIDIA Blackwell) versus GB200 NVL72 (NVIDIA Blackwell) 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 64 tok/s/user, the per-million math comes out to $0.21 for B300 and $0.06 for GB200 NVL72; GB200 NVL72 delivers 278% more output per dollar.
At 111 tok/s/user on DeepSeek V4 Pro 1.6T, B300 costs $0.41 per million tokens; GB200 NVL72 costs $0.08. GB200 NVL72 is 389% more cost-efficient at this operating point.
B300 edges GB200 NVL72 at 157 tok/s/user on DeepSeek V4 Pro 1.6T — $0.72 per million tokens versus $0.99, a 37% cost-per-token gap. (Numbers reflect the default 8k/1k · fp4 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 · GB200 NVL72 $1.86/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:$0.214GB200 NVL72:$0.057 | B300:$0.415GB200 NVL72:$0.085 | B300:$0.721GB200 NVL72:$0.990 |
| Concurrency | B300:~60GB200 NVL72:~9943 | B300:~8GB200 NVL72:~1815 | B300:~3GB200 NVL72:~41 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.