Tokens Per Watt and the Token Warehouse

PUE tells you how efficiently power reaches your GPUs. It does not tell you how efficiently that power becomes AI output. In this episode of Deep Geeks, we break down the metrics that do. https://www.weka.io/podcast/can-ai-survive-its-own-energy-appetite-deepgeeks-ep-1?utm_source=youtube&utm_medium=social The AI industry has spent years optimizing tokens per second and tokens per GPU. In this clip, WEKA and Nebius make the case that tokens per watt is the number that actually reflects whether an inference stack is efficient or wasteful. The analogy: Amazon is not legendary for its factories, it is legendary for its warehouses and delivery logistics. Inference today is all factory and no warehouse. Output gets produced and wasted before it ever reaches a user. Introducing a token warehouse layer, backed by real memory for tokens rather than just storage, changes the economics. Inefficient implementations can burn as much energy per chat or agent session as a household uses in a day. Efficient token warehousing cuts that by 80 percent or more. The conversation also covers where power usage effectiveness stops being useful, why energy per workload and energy per token deserve equal billing, and why software orchestration matters as much as chips and cooling. Idling GPUs are the quiet killer of efficiency, and no hardware spec sheet will tell you about them. Learn more: Listen to the full episode: https://www.weka.io/podcast/can-ai-survive-its-own-energy-appetite-deepgeeks-ep-1?utm_source=youtube&utm_medium=social&utm_content=full-episode WEKA NeuralMesh: https://www.weka.io/product/neuralmesh/?utm_source=youtube&utm_medium=social&utm_content=product-page Subscribe for more AI infrastructure content: https://www.youtube.com/@WekaIO?sub_confirmation=1