Latent power,
unlocked for AI

A new Metro Edge Inference Solution powered by Xeal.

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Explore the pod

1,600+

Open opportunity properties

160+

Metros

400+

Real estate partners

200+

Megawatts managed & growing

Two crises. One infrastructure gap.

AI needs power it can't get for years. Meanwhile, gigawatts of permitted, grid-connected capacity already sit idle inside America's buildings.

01 - AI power demand

93 GW Power needed for AI inference by 2030¹

3% Annual US grid growth - a structural deficit, not a delay²

4–7 yr Average time to build and power a new AI data center³

02 - Idle EV infrastructure

~90% Of installed capacity available for inference; concurrent EV load under 10%

1,600+ Properties live with permitted capacity idle today

1+ GW Upcoming managed power pipeline for colocation and EV charging

1 McKinsey, global AI inference data center demand rising from 20.9 GW in 2025 to 93.3 GW in 2030. 2 U.S. Energy Information Administration Short-Term Energy Outlook, January 2026: US electricity use forecast to grow 1% in 2026 and 3% in 2027. 3 PJM Interconnection data, 2026: AI infrastructure entering service in 2025 averaged over seven years to operation, with more than three years to an interconnection agreement and about four more to energization.

Inference is the new grid load

Demand

Inference has become the dominant workload

AI inference demand has roughly doubled every 6–12 months since 2023. Over 70% of AI compute is projected to shift from training to inference by 2027 - speed to power is now the bottleneck.

Regulation

Hyperscale is running into walls

New data center proposals face community opposition, and utility interconnection queues average five-plus years. Distributed, building-integrated infrastructure sidesteps every one of these blockers.

Latency

Real-time AI demands the edge

As AI shifts to agentic, voice, and video workflows, latency becomes a hard constraint. Centralized data centers add 20–500ms round-trip; metro-edge compute delivers sub-20ms responses they structurally cannot match.

Sources: Deloitte TMT Predictions (inference share of AI compute, 2025–2026); Gartner, AI-optimized IaaS forecast, August 2026 (inference spend overtaking training, 2026–2027); interconnection queue duration, Lawrence Berkeley National Laboratory, Queued 2025; edge vs. cloud round-trip latency, 2024 end-user measurement study cited in industry edge-infrastructure reporting.

The Xeal supply advantage

A grid that's live today.

200+ MW of managed electrical infrastructure and growing, live across 1,600+ properties in metro-edge communities. No transformer queue. No interconnect delay. The compute layer inherits it on day one.

Plan your cluster

Host your property

Introducing

Laitent Pods

Designed for the metro edge

A self-contained, rack-mounted pod housing up to 48 inference-grade NVIDIA GPUs - connected directly to power that is already permitted, inspected, and energized at the property.

Physically & digitally secure

Sovereign security across every pod, hardware to firmware.

Active cooling

Independent, built-in cooling keeps GPUs at peak utilization year-round.

Outdoor rated

NEMA 4X enclosure - sited in a parking bay or beside the utility room.

Quick install

Hours to operational - connecting directly to the existing panel.

Silent operation

Comparable to a normal conversation, safe for occupied buildings.

Low maintenance

Modular, self-contained design that lowers total cost of ownership.

Dynamic Power +
Distributed Compute

Idle EV charging power is seamlessly routed to Laitent Pods through Xeal's dynamic power orchestration - from a single slice of compute to a distributed cluster.

Distributed cluster Example properties

Pods across multiple properties operate as one seamless compute cluster.

Get early access

Host compute or get guaranteed capacity where you need it.

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Latent power,unlocked for AI