Training vs Inference Load
Training loads are large, flat and scheduled; inference loads are smaller-grained and variable — the burner base fits both, but each stresses a different backup layer.
Two workloads, two shapes
Training and inference sit at opposite ends of load behavior. Training is a small number of enormous, long-lived, near-constant jobs. Inference is a large number of short requests whose aggregate rises and falls with user demand across the day. The first is flat and planned; the second is spiky and stochastic.
The burner's firm base serves both, but they lean on different parts of the hybrid. Training's flatness is easy for a base source and hard for backup only at rare transitions. Inference's variability means the base holds the floor while the battery and grid follow the swings above it — a load-following role for the flexible layers rather than for the plasma.
This division matters because a fusion plasma does not like fast, deep power cycling; it prefers steady operation. So the design keeps the burner near a steady base output and lets storage and the grid absorb variability. A campus that mixes training and inference gets a stable base from the burner and a variable top from the flexible layers.
Neither workload changes the availability gate: both still require the burner's outages to be backed by independent layers, because both lose value when power drops unexpectedly.
- Training: flat, long, scheduled
- Inference: variable, diurnal, stochastic
- Burner holds the base; storage/grid follow the swings
- Steady-base operation suits the plasma; both workloads need outage backup