Cloud Wash Laundry Corporation logoCloud Wash Laundry CorporationDistributed AI infrastructure
EDGE AI • HEAT RECOVERY • WATER EFFICIENCY • DISTRIBUTED RESILIENCE • MICRO DATA CENTERS • COMMUNITY INFRASTRUCTURE • DARK-FIBRE SCALE-ACROSS • NVIDIA SPECTRUM-XGS • CHALLENGING MEGA DATA CENTRESEDGE AI • HEAT RECOVERY • WATER EFFICIENCY • DISTRIBUTED RESILIENCE • MICRO DATA CENTERS • COMMUNITY INFRASTRUCTURE • DARK-FIBRE SCALE-ACROSS • NVIDIA SPECTRUM-XGS • CHALLENGING MEGA DATA CENTRES
Reference carbon scenario

Reference carbon-mitigation calculation.

A current reference scenario estimates 294.4 metric tonnes of annual CO₂ mitigation for an average modeled site under the stated assumptions.

ComponentReference energyFactorAnnual mitigation
Server electricity switched from natural gas424,800 kWh0.40 kg CO₂/kWh169,920 kg
Thermal load displaced100,000 kWh0.20 kg CO₂/kWh20,000 kg
Recovered server heat382,320 kWh0.20 kg CO₂/kWh76,464 kg
Solar electricity70,000 kWh0.40 kg CO₂/kWh28,000 kg
Reference total294,384 kg = 294.4 t CO₂/year

This is a scenario model, not a verified project-wide inventory. Site-specific engineering, fuel displacement, climate and operating conditions determine realized results. The wider architecture also tests whether dedicated dark fibre and NVIDIA Spectrum-XGS can preserve workload performance across distributed heat-sink sites, because the environmental case is strongest if distribution can replace rather than merely supplement mega data centres.

Central architecture challenge

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.

Latest physical-model findings

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.

Distributed-AI paper PDFFanless-PVT paper PDF
79.4%IT electricity converted to useful process heat in the optimistic fully temperature-compatible heat-sink bound
77.6%same-supply operational emissions reduction at 43 gCO₂e/kWh in that upper-bound compatibility case
74 m³/yrcentral direct evaporative water in the current Los Angeles screening model
44.2%reduction in wetted states under dispatch in the current fanless heat-rejection screening figure
120 / 67 stateswater-eligible states versus wetted states under dispatch in the current screening summary
3-site dark-fibre pilotplanned workload-level validation of the NVIDIA Spectrum-XGS scale-across architecture against a co-located reference

The 79.4% and 77.6% values use the optimistic fT = 1.00 temperature-compatibility bound; lower compatible heat fractions materially reduce both benefits. The updated fanless heat-rejection figure also summarizes 83 of 144 states as feasible overall and 77 of 144 as feasible by free pathways alone, while array-wide EHD is not preferred because only 3 of 80 tested cases exceed the conservative comparator.