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 43 tok/s/user, the per-million math comes out to $0.30 for B300 and $0.26 for GB200 NVL72; GB200 NVL72 delivers 19% more output per dollar.
At 79 tok/s/user on DeepSeek V4 Pro 1.6T, B300 costs $0.51 per million tokens; GB200 NVL72 costs $1.05. B300 is 106% more cost-efficient at this operating point.
B300 edges GB200 NVL72 at 116 tok/s/user on DeepSeek V4 Pro 1.6T — $1.00 per million tokens versus $3.37, a 237% 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.)
GPU pricing (owning hyperscaler): B300 $2.34/GPU/hr · GB200 NVL72 $2.21/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 | B300:$0.305GB200 NVL72:$0.257 | B300:$0.511GB200 NVL72:$1.051 | B300:$0.999GB200 NVL72:$3.368 |
| Concurrency | B300:~28GB200 NVL72:~125 | B300:~8GB200 NVL72:~47 | B300:~4GB200 NVL72:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.