Compute Energy
When a Megawatt-Hour Becomes Computation
Computational demand is now a power-system question. Siting, interconnection, capacity, curtailment and price shape what AI infrastructure can actually do — and what it costs to do it.
No compute architecture is complete until the power architecture is understood.
The Relationship
Two economies, one input
Electricity has a market price. Computation has a market price. They move independently, and the same unit of energy can be presented to either one.
Golden Eagle Energy Group approaches compute as an energy-consuming asset class first. Where energy can be acquired on favorable terms and a workload can tolerate flexible scheduling, computation becomes a candidate destination for that energy rather than an unrelated line of business.
That evaluation runs in both directions: some hours favor selling or storing energy, and some favor converting it. The discipline is in knowing which is which.
See the commercial modelFlexible Workloads
Computation that can wait is computation that can be placed
Workloads differ in how tightly they are bound to a specific hour. Deferrable work is the natural pairing for opportunistically acquired energy.
- 01
AI training
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Batch inference
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Model processing
- 04
Synthetic-data generation
- 05
Rendering
- 06
Indexing
- 07
Embeddings
- 08
Other deferrable computational workloads