From Passive to Active: How Colocation Is Becoming an AI Optimization Engine

From Passive to Active: How Colocation Is Becoming an AI Optimization Engine

Authored by Derek Gillespie, Chief Executive Officer

For years, colocation was defined by what it didn’t do. It didn’t compute, optimize, or touch the workload itself. A customer brought in equipment, the facility provided space, power, and cooling, and the relationship largely ended there. That model is changing quickly, and AI is the reason.

As AI workloads push power and thermal demands beyond what legacy facilities were designed for, colocation providers are being asked to do more than host equipment. They’re being asked to actively shape how that equipment performs. The shift from passive real estate to active infrastructure is one of the more consequential changes happening in digital infrastructure today.

The density problem

A decade ago, a typical enterprise rack drew somewhere in the range of 5 to 10 kW. According to AFCOM’s 2026 State of the Data Center Report, cited by Upsite Technologies, average rack density climbed from 7 kW in 2021 to 16 kW in 2025, then nearly doubled again to 27 kW in 2026, a roughly fourfold increase in just three years. Separately, Uptime Institute’s 2026 Global Data Center Survey found a growing share of operators now reporting peak rack densities of 30 kW or higher to support advanced computing workloads, while AI training clusters built around the latest GPU platforms can require 100 kW per rack or more.

That kind of jump isn’t absorbed by simply adding circuits. It requires rethinking power distribution, cooling, and floor design from the ground up. Air cooling generally tops out around 40 to 50 kW per rack; beyond that, direct-to-chip and liquid cooling becomes necessary rather than optional. It’s a range we see reflected in day-to-day deployments: At 365 Data Centers, cabinet configurations today span roughly 50 to 160 kW, supported by liquid-cooled environments, depending on what a given workload demands.

This is where colocation stops being a neutral container and becomes an active variable in AI performance. A facility that delivers dense, reliable power and modern cooling directly affects how efficiently a customer’s workloads run. One that can’t do so becomes a bottleneck, regardless of how advanced the customer’s hardware is.

From hosting equipment to enabling performance

The practical implications of this shift show up in a few areas.

Power design now has to plan for variability, not just capacity. AI training creates sharp, uneven power draws as GPUs cycle between full utilization and idle states, a different pattern than the steady loads traditional applications produce. Facilities built only for consistent draw can struggle with that fluctuation.

Cooling has become a crucial design decision. Since not every workload needs the same thermal approach, many operators are deploying hybrid environments mixing air-cooled and liquid-cooled solutions within the same facility. That flexibility, more than any single cooling method, is becoming the more durable strategy.

Speed to capacity matters as much as capacity itself. Enterprises evaluating AI infrastructure often work on compressed timelines, and the choice increasingly comes down to retrofitting existing space versus waiting on new construction. Both paths are valid, but they serve different levels of urgency.

What this means for your infrastructure

Not every workload needs the most extreme density available. Inference workloads, which serve real-time AI responses, often run comfortably at 10 to 20 kW per rack, well within reach of many upgraded facilities. Training workloads are a different story, and that distinction matters when evaluating what a project actually requires.

The broader takeaway: Colocation’s role has expanded. It is no longer just a place to put equipment; it is a factor that directly influences whether AI infrastructure performs as intended. At 365 Data Centers, we’ve treated power and cooling upgrades as a design problem specific to each facility, not a one-size-fits-all retrofit.

As AI workloads evolve, the facilities that can adapt power delivery, cooling strategy, and deployment speed will matter most to the enterprises building on them. Contact us today at https://365datacenters.com/contact/ to discuss what our secure, reliable colocation services can do for you.

The Importance of Low-Latency Connections in Modern Business Applications

The Importance of Low-Latency Connections in Modern Business Applications

Authored by James Janssen, VP of Strategic Projects

In today’s digital economy, milliseconds matter. Sites that load in 1 second achieve conversion rates nearly 3 times higher than those that load in 5 seconds, according to digital marketing agency Portent. As enterprises increasingly rely on real-time applications – from AI inference and video collaboration to financial transactions and remote operations – low-latency connectivity has moved from a technical nicety to a business imperative.

What Low Latency Means for Business

Latency is the time it takes for data to travel from source to destination and back. Low latency means minimal delay, enabling applications to respond instantly and workflows to run smoothly. For modern enterprises, this translates to faster decision-making, better user experiences, and more reliable operations across distributed teams and systems.

Unlike bandwidth, which measures how much data can move at once, latency measures how quickly data moves. High bandwidth with high latency is like a wide highway with frequent stoplights; capacity exists, but responsiveness suffers. For time-sensitive applications, low latency is non-negotiable.

Industries Where Latency Drives Outcomes

Several sectors illustrate why low-latency infrastructure is critical:

  • Financial services: High-frequency trading platforms depend on microsecond-level execution. A 5-millisecond advantage can mean the difference between profit and loss on algorithmic trades.
  • Health care: Telemedicine, remote diagnostics, and AI-assisted imaging require near-instant data transfer to support time-critical clinical decisions.
  • Manufacturing and logistics: Real-time inventory tracking, robotics coordination, and supply chain visibility depend on stable, low-latency networks to prevent costly delays.
  • AI and machine learning: AI inference – the process of running trained models on live data – is highly latency sensitive, as reported by MarketsandMarkets. Applications like chatbots, fraud detection, and predictive analytics lose value if responses lag.

The Infrastructure Behind Low Latency

Achieving low latency requires more than fast internet. It demands purpose-built infrastructure that minimizes every source of delay. Edge proximity is critical, placing compute and storage closer to end users reduces the physical distance data must travel, which directly cuts round-trip time. High-density interconnection plays an equally important role. Data centers that serve as interconnection hubs enable direct, private paths between networks, cloud providers, and enterprises, avoiding the public internet’s congestion and unpredictable routing, as the Brookings Institution reports.

Supporting this performance requires advanced cooling and power systems. High-performance networking equipment generates significant heat and depends on stable, high-density power to maintain consistent throughput without thermal throttling or outages. Finally, redundant fiber routes are essential for resilience. At 365 Data Centers, we know diverse network paths prevent single points of failure and ensure latency remains low even during fiber cuts, maintenance, or other disruptions.

Practical Steps for Enterprises

Organizations seeking to optimize latency should start by auditing application performance to identify which workloads are truly latency-sensitive and establish baseline response times. From there, data center location becomes a strategic lever: Proximity to end users, cloud on-ramps, and major network exchanges can dramatically reduce latency by shortening the physical path data must travel.

Direct interconnection should be prioritized over public internet routing. Private connections to cloud providers and key partners eliminate unnecessary hops, reduce jitter, and improve reliability for time-critical applications. Finally, infrastructure planning must account for AI workloads. AI inference is highly latency-sensitive, according to MarketsandMarkets, especially for real-time use cases like customer service chatbots, fraud detection, and predictive maintenance, where delayed responses directly diminish business value.

The Bottom Line

Low-latency connectivity is no longer optional for enterprises that depend on real-time applications. As AI, automation, and distributed operations become standard, the infrastructure that supports them must deliver consistent, predictable performance.

At 365 Data Centers, we design our facilities around the needs of latency-sensitive workloads, from high-density power and advanced cooling to direct cloud on-ramps and redundant fiber networks. Our goal is to give enterprises the infrastructure foundation they need to run modern applications with confidence. Contact us today at https://365datacenters.com/contact/.