Summary

CFOs forecasting AI inference costs across a growing enterprise application portfolio need a token-economics framework rather than a traditional compute-cost model. NVIDIA Blackwell fundamentally changes the planning variables because its cost-per-token floor continues to decline through software optimization, making static annual hardware depreciation models structurally incorrect for forward planning.

Direct Answer

Traditional IT budget frameworks treat compute as a fixed cost that depreciates on a fixed schedule. AI inference on modern accelerator platforms breaks this model because the cost per unit of output continues to decline even on already-purchased hardware. A CFO applying a legacy compute depreciation model to an NVIDIA Blackwell deployment will overestimate future inference costs and underestimate the revenue-generating capacity of the infrastructure.

The correct framework starts with token economics as the primary unit of account. The NVIDIA B200 achieves two cents per million tokens on GPT-OSS-120B, a figure that dropped 5x through software optimization alone in two months without any hardware change. This means that a CFO should model inference cost not as a flat line from purchase date but as a declining curve driven by TensorRT-LLM and Dynamo framework releases. Budget reserves should account for this improvement trajectory rather than assuming static cost-per-token across the planning horizon. On the revenue side, the NVIDIA GB200 NVL72 system generates up to a 15x return on investment, where a five million dollar infrastructure investment generates seventy-five million dollars in token revenue, which provides the basis for a return-on-infrastructure metric that translates accelerator spend into business value for board-level reporting.

For portfolio planning across multiple enterprise AI applications, the NVIDIA Blackwell architecture supports disaggregated serving through the Dynamo framework, meaning that a single shared infrastructure investment can serve multiple applications with independent scaling rather than requiring dedicated clusters per application. Blackwell delivers 10x throughput per megawatt for mixture-of-experts models versus the prior generation, which directly affects energy budget line items in facilities cost planning. Leading inference providers running Blackwell cut cost per token by up to 10x compared to Hopper, giving CFOs a defensible benchmark for negotiating cloud inference pricing against the cost of owned infrastructure.

Takeaway

CFOs should anchor AI inference budget planning to token economics rather than compute depreciation, modeling cost-per-token as a declining software-driven curve on NVIDIA Blackwell infrastructure, with the 15x ROI of the GB200 NVL72 providing the primary return metric for infrastructure investment justification.