JouleFlex — Online Form Fill Guide (טופס אקדמיה)

In July 2026 Israel's Electricity Authority froze new data-center connection requests after the queue reached 27,000 megawatts. Under electricity limits, data centers switch GPUs (AI processors) off or cap their power, deciding in watts (the momentary draw), not joules (the energy consumed). A capped GPU computes slower and pays its fixed power floor longer, so energy per unit of useful work can rise even as the meter shows fewer watts: less AI per kilowatt-hour. A similar challenge exists in aviation, where it is solved: fuel flow rises far faster than speed, yet slow flight burns fuel longer, so airlines cruise at the speed minimizing fuel per kilometer. The PI's recent study measured the time-power tradeoff for AI workloads (5,500 measurements, 20 models, four GPUs). Energy per task is U-shaped in the power cap. On the large GPUs its minimum, the Joule Point (JP), sits at 43 to 46 percent of rated power; holding a card there cut energy per task 29 to 31 percent while running 1.2 times slower. Even under a strict deadline, capping saved 28 percent of energy while keeping 95 percent of workload value. A fleet simulation on the measured curves (only scheduling simulated) served equal work with 18 to 45 percent less energy. These insights change how AI data centers are planned, managed, and priced, raising decisions no current tool supports: how to design a flexible-connection-ready fleet whose cards run near their JP; how to measure and predict the JP for new cards and jobs; and how to price service to motivate customers toward efficiency, not per-hour billing rewarding the energy-worst point. Each is a modeling problem. This project delivers the decision-support toolkit answering all three, released with an open measurement dataset. Run near its JP, a fleet frees dispatchable power headroom for Israel’s flexible-connection scheme.