Enterprises must shift focus from raw compute metrics to the real cost of producing AI output: tokens. Traditional measures—GPU hourly rates, peak petaflops or FLOPS per dollar—only capture inputs. What drives profitability and scale is cost per token, calculated as total infrastructure expense divided by tokens generated. Every layer of the stack—hardware performance, network design, precision formats, runtime optimizations and workload support—feeds into delivered tokens per second, which in turn lowers cost per token and boosts revenue per megawatt. In practice, reducing token cost means squeezing more output from each GPU and each watt. Optimizations like FP4 precision, speculative decoding, KV-cache offloading and disaggregated serving aren’t optional extras; they’re required to push that denominator up. Without them, a cheaper GPU might sound good on paper but ends up costing far more per million tokens. On top of inference tweaks, supporting the full AI lifecycle—from training through large-scale inference—keeps hardware busy and infrastructure fungible. NVIDIA’s latest Blackwell platform highlights the gap between input and output metrics. Its GB300 NVL72 setup lists at $2.65 per GPU-hour versus $1.41 on the Hopper-based HGX H200. By FLOPS per dollar, Blackwell is twice as efficient. But real-world tests tell a different story: Blackwell pumps out 6,000 tokens per second per GPU—65 times Hopper’s 90—translating to 2.8 million tokens per megawatt, a 50× boost. That drives cost per million tokens down from $4.20 on Hopper to $0.12 on Blackwell, a 35× improvement. Any AI infrastructure decision must anchor on cost per token and delivered token throughput, not just chip specs. Open-source inference stacks like vLLM and TensorRT-LLM keep chipping away at token costs on existing hardware. Cloud partners such as CoreWeave and Together AI already leverage Blackwell to push economies of scale, proving that optimized AI token factories, not generic data centers, define modern AI economics.