A white Standard Pod compute enclosure installed on the rooftop of a commercial building with a city skyline behind it

Agent-operated edge inference

Idle power in. AI compute out.

Standard Compute turns energized building capacity into GPU inference at the metro edge - sited, dispatched, and monitored around the clock by AI agents.

Reserve capacity See the agents

Autonomous fleet monitoring: 24/7
Metro-edge latency: <20ms
Contract to live inference: Weeks

Energized across major grid markets

ERCOT
PJM
CAISO
NYISO
MISO
SPP

Standard Compute turns permitted, energized building power into distributed AI inference in weeks, not years. Our agents find the idle capacity, place the workloads, and watch every GPU around the clock - all on the Standard Power grid that is live today.

1,600+
Energized properties: 160+
Metros: 400+
Host property partners: 200+
Megawatts managed & growing

AI is waiting on power. Buildings already have it.

New data centers wait years for a grid connection. Meanwhile, buildings across the country have electrical service sized for peaks they rarely reach. That capacity is already permitted, connected, and paid for.

What AI needs

93 GWOf power for AI inference worldwide by 2030, up from 21 GW in 2025¹
3%Forecast growth in US electricity demand in 2027. New supply isn’t keeping pace²
7 yrFrom proposal to operation for AI infrastructure that came online in 2025³

What's already built

~90%Of a typical site’s electrical service goes unused. Actual load runs under 10%
1,600+Properties we manage, with service already permitted and connected
1+ GWOf additional capacity in our pipeline for compute

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.

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 (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.

The Standard Power Advantage

A grid that's live today.

We manage 200+ MW of electrical service across 1,600+ properties at the edges of major metros. It's already permitted and connected, so compute placed there skips the interconnection queue and the wait for new transformers. Racks go live on power that's already there.

Plan your cluster Host your property

AI Agent Architecture

Compute that runs itself

Three specialized agents work together across the full lifecycle of edge inference, from finding idle power to placing workloads to keeping every GPU healthy. No NOC required.

Siting Agent

Capacity discovery, site scoring & provisioning

Scans every energized property in the Standard Power network, measures real headroom against historic load, scores each site for latency and demand, and provisions pods where capacity is idle - without a transformer queue.

Headroom scanInterval data across 1,600+ properties
Latency scoringProximity to metro demand centers
Permit checkExisting electrical permits validated
Auto-provisionPod spec matched to site capacity

Sites scanned: 1,600+
Site assessment: < 48 hr
Headroom accuracy: 99.5%

Scanning energized sites...live

Oakland, CA - 480V / 1.2 MWQualified
Palo Alto, CA - 208V / 640 kWScoring
San Francisco, CA - 480V / 900 kWQueued

2 pods provisioned this week

Introducing

Standard Pods

A white Standard Pod installed beside a commercial building's loading dock, wired directly into the utility room

Inside a Standard Pod: a rack of GPU servers with tidy cabling and status lights

Built for the building next door

A self-contained pod housing up to 48 inference-grade GPUs, wired into power that is already permitted and energized - then handed to our agents to operate from day one.

Agent-monitored

Every GPU, breaker, and cooling loop watched 24/7 by the Operations Agent.

Secure by design

Locked, sensor-monitored enclosure with signed firmware and encrypted telemetry.

Closed-loop cooling

Self-contained cooling keeps GPUs at full clocks through summer peaks.

Outdoor rated

Weatherproof enclosure for rooftops, loading docks, and parking bays.

Plugs into existing power

Connects to the panel already permitted and energized at the property.

Quiet neighbor

Low-noise operation that is safe beside occupied buildings.

The Standard Compute edge controller, a compact fanless aluminum gateway with network ports and status lights

Standard Edge Controller

The on-site brain inside every pod.

Each pod ships with a ruggedized edge controller that runs our dispatch and operations agents locally. It reads the building's electrical panel in real time, throttles GPUs before a breaker ever trips, and keeps control decisions on-site - even if the uplink drops.

Local agent execution
Building-safe power limits
Data stays on-prem

Connectivity: 2x 10GbE, LTE failover
Interfaces: Modbus TCP, BACnet, RS-485
Telemetry: Breaker, thermal, GPU, access
Deployment: In-pod, fanless edge

Dynamic Power + Distributed Compute

The Dispatch Agent routes idle, energized power to Standard Pods in real time - from a single slice of a GPU to a cluster spanning an entire metro.

Slice Of Compute

Even a fractional slice of compute is available.

Individual GPU

Allocation to individual GPUs for fine-grained control.

Single Server

Power is routed to one server inside the pod.

Distributed Cluster

Example Bay Area properties

San Francisco900 kW

Oakland1.2 MW

Palo Alto640 kW

Fremont1.4 MW

San Jose1.8 MW

Pods across multiple properties operate as one seamless compute cluster.

Early access

Get early access

Reserve agent-operated GPU capacity in your metro, or host a pod and earn from power your building already has.