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

People behind the platform

Meet the team

Engineering, finance, IP, cloud systems and operations brought together around distributed infrastructure, heat reuse and applied AI.

The team combines expertise in engineering, finance, intellectual property, cloud systems, cybersecurity, control systems and operations.

Jonathan Bily

CEO • Computer Electronics & Cybersecurity

Gabriele Ferrari

Fluid Mechanics Engineer

Chuang Kou

Intellectual Property Lawyer

Dr. Ajeet Kumar

Finance & Economics Professor

Flavio Ymeri

Cloud & Network Engineer

Dr. Muhammad Mahmoud

Electrical Engineer

Dr. Usama Khan

Computer Electronics & Cybersecurity

Glyn Merriman

Retrofit Team Leader

Fahad Muhammed

SaaS Specialist

Khaled Haddad

Control Systems Engineer

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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 PDF Fanless-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.