24/7 Power For AI Compute

Model training and inference run continuously, so the power source must run continuously too — the burner is designed for exactly that duty cycle.

Why compute never sleeps

A large training job is a single distributed computation spread across thousands of accelerators. Pausing it wastes the partial work and idles enormously expensive hardware, so operators keep clusters saturated for the duration of a run. Inference has the opposite reason for the same result: user requests arrive at all hours across time zones, so serving capacity stays hot continuously.

This is why an intermittent-first supply is awkward for compute and why a firm source is a natural fit. The burner's plasma, when sustained, yields a steady charged-particle stream that direct energy conversion turns into steady current. There is no fuel-delivery weather, no diurnal cycle, and no seasonal derate.

The limit is duty continuity across outages, not the steadiness of a single burn. Real plants stop for maintenance and for faults. The burner's modelled availability of 0.86-0.995 means multiple hours to weeks of downtime per year depending on where in that range it lands. A training run cannot tolerate an unannounced multi-day stop, so the source must be backed by ride-through storage and a grid tie The practical rule is that continuity is a property of the whole supply, not of the burn alone, so the operator engineers backup for every hour the burner might be absent rather than assuming the plasma will always be present.

• Training saturates clusters for weeks; pausing wastes work
• Inference is continuous across time zones
• Burner output is steady while burning
• Outage coverage — not burn steadiness — is the real 24/7 challenge