DCDATA's distributed fleet delivers rapidly deployable, hour-matched carbon-free GPU capacity, placed close to end users — a credible way to meet 24×7 CFE and ESG commitments with auditable data, not annual offsets.
Compute demand scales in months, chip supply scales in quarters, but grid infrastructure scales in years — the industry's "hyperscaler paradox." A second, tightening constraint sits alongside raw power availability: the credibility of carbon claims.
Utility interconnection queues in the US are measured in years; EU/UK transmission upgrades face similarly long lead times; Australian renewable energy zones are already congested.
Major buyers of compute and energy are moving to prove clean generation and storage are available in the same hour and location as the load — very hard from a centralised grid connection alone.
A fleet of solar-plus-battery-plus-compute Nodes is built precisely to deliver this profile — independently verifiable at the household level, at fleet scale.
| Centralised model | DCDATA model |
|---|---|
| Large single-site interconnect; multi-year permitting and energisation | Modular Nodes deployed incrementally, in parallel, without new bulk interconnects |
| Renewable matching typically done annually via certificates | Solar and battery co-located with compute enable real, hour-matched 24×7 CFE |
| Single point of failure | Thousands of independent Nodes remove single points of failure |
| New load competes with existing grid users for scarce capacity | Every Node adds — not consumes — net grid capacity |
DCDATA is not a replacement for hyperscale training infrastructure, which remains essential for the largest, most tightly coupled AI training clusters. It complements centralised infrastructure for workloads that benefit from proximity, rapid deployment and a hard carbon-free guarantee.
Modular, latency-sensitive, and the fastest-growing compute category.
Proximity to players directly improves experience and reduces backbone congestion.
Bursty, parallelisable workloads scheduled flexibly across the fleet.
Hour-matched, auditable clean energy for customers under 24×7 CFE mandates.
Modular, liquid-cooled GPU compute module purpose-built for AI inference, rendering and other distributed, latency-sensitive workloads.
Closed-loop liquid cooling, quiet enough for residential and small-business operation — no bulk evaporative or open-loop water draw.
Whole-site battery sized to power the Node's compute load and leave meaningful backup and grid-services headroom.
DCOS continuously verifies and publishes 24×7 CFE performance, matching generation, storage and consumption on an hourly, Node-by-Node basis. That's the data you take to your regulators, your investors, and your own customers.
| Node | Region | Generation | Battery SoC | Compute draw | 24×7 CFE |
|---|
We're currently validating DCOS orchestration and 24×7 CFE measurement in an Australian pilot fleet, with capacity opening for early offtake partners.