Micro data centers

Compact, repeatable, distributed micro data centers.

Cloud Wash Laundry focuses on smaller, repeatable deployments rather than giant greenfield builds, aiming to lower infrastructure friction and shorten deployment paths. The architecture is intentionally paired with dedicated dark-fibre links and NVIDIA Spectrum-XGS scale-across networking so the network can test whether aggregated micro data centres can replace the functional role of a mega data centre.

Why this approach

Uses existing facilities rather than waiting for large new campuses.
Puts compute near customers and real-world demand centers.
Allows staged growth across many markets instead of a single mega-build.
Makes heat reuse practical because thermal loads are colocated with compute.
Creates more optionality for local partners and franchisees.

Key supporting visuals

Parent unit economics scenarios — **Unit economics scenarios**Page from the parent business plan summarizing four franchisee-site scenarios.

**Owned-site scenario analysis**Franchisee plan page comparing performance when the site is owned by the franchisee.

**Leased-site scenario analysis**Franchisee plan page comparing performance when lease payments are included.

Network milestones slide — **Milestones**Network slide summarizing staged growth and rollout timing.

Why micro matters thermally

Roof-area matching becomes favorable precisely where compute is small and useful heat is local.

The current PVT deployment screen estimates about 502 W/m² of dry heat rejection and 661 W/m² at the wet reference point. A micro installation co-located with a laundromat requires only about 90 W/m² of usable roof and a car wash about 58 W/m², so the modeled rejection load is fully covered using roughly 18% and 12% of available roof, respectively.

At the other extreme, a conventional hyperscale facility reaches only about 28.2% dry roof coverage and a GB200-class AI training hall about 5.2%. The current model therefore supports PVT as a particularly strong micro-site architecture, while treating it mainly as a partial water-saving supplement at hyperscale.

Open science downloads

Current papers, methods and model limits.

First page of the current heat-sink-constrained distributed AI infrastructure manuscript

Heat-sink-constrained siting of distributed AI infrastructure: energy, carbon and water impacts in Los Angeles

The current deterministic hourly study places 6,160 B300-equivalent accelerators across 120 representative car-wash and laundromat hosts and tests useful heat, carbon, water, climate and metropolitan-network constraints. Its network programme uses NVIDIA Spectrum-XGS as the scale-across layer over dedicated optical fibre and specifies fibre-spool and multi-site experiments to test whether distributed sites can match co-located clusters — the key experiment behind the project’s objective of proving that mega data centres can become obsolete as the default architecture for many workloads.

Current paper PDF Model + files

First page of the current mechanism-resolved fanless photovoltaic-thermal heat-rejection manuscript

Mechanism-resolved modeling and adaptive control of a fanless photovoltaic-thermal heat-rejection array

The current 170 m² roof-array study resolves passive and wetted mechanisms separately, quantifies the water-capacity trade-off, rejects array-wide ionic wind on energetic grounds and tests roof-area matching from micro facilities to hyperscale.

Current paper PDF DOCX Model + files

The goal: prove the mega data centre is no longer necessary.

Cloud Wash Laundry is being designed around a direct technical challenge to the conventional mega-campus model: place compute where useful heat already exists, then connect those heat-sink-sited AI factories with dedicated dark fibre and NVIDIA Spectrum-XGS scale-across networking so the distributed sites can operate as one metropolitan compute fabric.

The dark fibre is the dedicated optical transport; Spectrum-XGS is NVIDIA’s scale-across networking layer. The project’s proposed fibre-spool testbed and multi-site field pilot are intended to determine whether distributed sites can match co-located clusters on workload-level measures such as time-to-result and energy per token.

Measured next. Modeled now. Heat-sink constrained.

Current design-stage results combine the 120-host Los Angeles heat-sink-siting model with the updated fanless heat-rejection screening figure. They are screening outputs, not field-certified performance. In parallel, the network programme tests whether NVIDIA Spectrum-XGS scale-across networking over dedicated dark fibre can make geographically separated AI factories behave as one compute system. The mega-data-centre conclusion remains an experimental objective, not an already validated result.