Introducing Infill AI
AI Compute at The Edge of The Edge™
Predictable cost. Consistent performance. Reliable operation.
Arctevity’s Infill AI is a new class of urban AI infrastructure built for production inference.
Infill AI is purpose-built, proximity-optimized AI infrastructure for production inference workloads, delivering predictable cost, consistent performance, and reliable operation.
Infill AI embeds inference compute capacity within existing occupied buildings and operates those sites together as resilient city-scale inference clusters.
By leveraging underutilized urban space and fragmented electrical capacity, Infill AI creates new AI infrastructure from existing city resources.
With Infill AI, every city can participate in the AI economy.
AI demand is shifting from training to production inference – AI doing useful work – AI with consequences.
As AI moves from model training to production inference, organizations need infrastructure that delivers predictable cost, consistent performance, and reliable operation close to where AI is used.
Inference changes the nature of AI compute and activates new opportunities. It is not “smaller” training, it is a different class of AI compute that creates opportunities for new architectures, new operating models, and new business models.
Production inference activates a new infrastructure layer that:
Is more than just compute
Thrives on proximity
Is being built — city by city — as Infill AI
The AI Infrastructure Gap.
Training builds models. Inference operates the world. By analogy:
Training is the refinery producing fuel at LLMs: appropriately massive, centralized, capital-intensive.
Inference is the everyday consumption of LLMs — many uses, many places, all the time.
The refineries exist. The delivery layer for production inference does not.
Think about it … you go to a gas station, not a refinery, to fill up your car!
Inference is rapidly becoming the dominant AI workload.
Driven by enterprises, healthcare systems, universities, and the public sector, demand for generative, agentic, and physical AI is concentrating in cities.
Distributed inference — AI demand is shifting from centralized training toward inference workloads deployed across many locations closer to users and data
Latency, locality, reliability — Inference workloads require fast response times, proximity to users and data, and predictable operation
Urban readiness — Cities contain the buildings, power, and fiber needed for AI infrastructure but lack purpose-built inference infrastructure
Infill AI deploys city-scale clusters of neighborhood-scale AI sites designed specifically for inference workloads. Each site contributes inference capacity to a city-scale cluster that delivers on-premises-like latency and control without requiring customers to own or operate AI infrastructure themselves.
Dedicated urban inference infrastructure for production inference workloads.
Urban & repurposed — Existing buildings leveraged as AI infrastructure
Proximate — Inference compute located near users, data, and institutions
Autonomous — AI-operated infrastructure with minimal on-site staffing
Scalable — Cluster expansion city by city
Designed for cities. Built for autonomy.
Infill AI focuses on cities with active business communities, academic institutions, healthcare systems, and municipal operations—places where AI demand exists but hyperscale infrastructure is distant or constrained.
Each Infill AI site is created as a standardized “building within a building,” allowing AI infrastructure (compute, power, cooling, and networking) to be rapidly deployed inside existing commercial buildings with minimal disruption to occupants and the surrounding communities.
Neighborhood-scale — 0.25–2MW GPU-power urban sites sized for low-latency inference, not remote hyperscale training
Autonomous operations — Fully AI-operated infrastructure that reduces staffing and operating costs
Clustered resilience — City-level resilience through coordinated clusters of sites rather than a single massive facility
Fragmented Urban Power → City-Scale AI infrastructure that works with cities, not against them.
Infill AI is designed to integrate cleanly into urban environments rather than overwhelm them. By distributing AI inference compute across smaller sites, Infill AI reduces strain on city infrastructure, turning inference compute into essential urban infrastructure.
Lower urban stress — Smaller, distributed sites reduce pressure on power infrastructure, cooling water, and permitting pipelines
Reuse over sprawl — Reactivates underutilized urban real estate instead of consuming new land
Practical energy reuse — Closed-loop cooling enables reuse of GPU waste heat for co-located tenants and adjacent buildings
Arctevity — The company behind Infill AI.
Infill AI city clusters are created and operated by Arctevity. Arctevity’s ArcX™ platform is the foundation that makes Infill AI sites and clusters - which are fully AI-operated - practical and scalable.
We are assembling strategic relationships with infrastructure providers, commercial real estate owners, utilities, and institutional organizations to support future city-scale deployments.
The result is AI infrastructure that delivers predictable-cost, consistent-performance inference for clients while creating shared value for building owners, local institutions, and the communities they serve.
Infill AI is the urban infrastructure for the inference era—deployed quietly, incrementally, and in the cities where AI is used.
If you are a building owner, developer, city stakeholder, utility, potential partner, or interested in using Infill AI for low-latency inference compute, we welcome a conversation. Email us at info@arctevity.com.