What is GPU Colocation? The Complete Guide

If you are building, training, or serving AI models, you have almost certainly encountered the GPU capacity problem: cloud instances are expensive, availability is unpredictable, and you have limited control over the hardware. GPU colocation offers a different path. You buy or lease your own GPU servers and house them in a purpose-built data centre that provides the power, cooling, network connectivity, and physical security your hardware needs.

This guide explains exactly what GPU colocation is, how it works in practice, who benefits from it, and how it compares to alternatives like public cloud and dedicated GPU servers. Whether you are an AI startup evaluating infrastructure options or an enterprise ML team looking to reduce cloud spend, this is the practical reference you need.

What GPU Colocation Actually Means

GPU colocation is the practice of placing your own GPU hardware -- servers, networking switches, storage arrays -- inside a third-party data centre. The data centre operator (the colocation provider) supplies the facility: physical space in a rack, electrical power, cooling infrastructure, network connectivity, and round-the-clock physical security. You retain full ownership of and control over the hardware itself.

The concept is identical to traditional server colocation, which has existed since the 1990s. The critical difference is that GPU workloads have fundamentally different infrastructure requirements. A single rack of NVIDIA H100 or H200 servers can draw 30-50kW of power, compared to the 5-10kW that a traditional enterprise server rack uses. That factor-of-five increase in power density changes everything: the electrical distribution, the cooling technology, the rack design, and the facility economics.

Not every colocation provider can handle GPU workloads. Traditional data centres were engineered for enterprise IT loads that are relatively cool and power-efficient. GPU colocation requires facilities that have been specifically designed or retrofitted for high-density computing, with liquid cooling infrastructure, reinforced power distribution, and high-bandwidth network connectivity.

How GPU Colocation Works in Practice

The process of colocating GPU hardware follows a predictable sequence, though the details vary by provider and scale:

1. Capacity Assessment and Provider Selection

You start by defining your requirements: how many GPUs, what power envelope, what cooling method, what network connectivity, and what physical location. A broker like ColoGPU can match these requirements against available capacity from verified providers, or you can contact facilities directly. The key constraint in 2026 is not cost but availability -- high-density, liquid-cooled rack space is genuinely scarce in most markets.

2. Contract and Provisioning

Colocation contracts typically run 12-36 months for GPU deployments. The contract specifies your power allocation (measured in kilowatts), rack space (measured in rack units or full racks), cooling capacity, network cross-connects, and service-level agreements (SLAs) for uptime and support. Pricing structures vary, but most providers charge primarily based on power consumption.

3. Hardware Procurement and Deployment

You procure your own GPU servers (e.g., NVIDIA DGX, Supermicro, Dell PowerEdge with GPU accelerators) and either ship them to the data centre or arrange on-site delivery. Many providers offer "remote hands" services to physically rack and cable your equipment. Some also offer hardware procurement assistance or partnerships with server vendors.

4. Network and Connectivity

You establish network connectivity through cross-connects to your chosen carriers, internet exchanges, or direct cloud on-ramps. For multi-rack GPU clusters, InfiniBand or high-speed Ethernet interconnects between your servers are critical for training workloads that require low-latency GPU-to-GPU communication.

5. Ongoing Operations

Once deployed, you manage your hardware remotely through out-of-band management interfaces (IPMI, iDRAC, BMC). The colocation provider handles facility operations: power delivery, cooling, physical security, and maintenance of shared infrastructure. You handle everything inside your rack: operating systems, drivers, workload scheduling, and hardware maintenance (or contract with the provider for managed services).

Who Needs GPU Colocation?

GPU colocation is not the right answer for everyone. It makes the most economic and operational sense for specific profiles:

AI Startups with Steady Compute Needs

If you are running training jobs or inference services for 12+ hours per day, the cost equation tilts sharply in favour of owned hardware in colocation versus cloud. Startups that have moved past the experimentation phase and have predictable GPU demand are the primary candidates.

Machine Learning Engineering Teams

Enterprise ML teams that need guaranteed access to specific GPU hardware -- particularly for fine-tuning large language models or running production inference at scale -- benefit from the control and availability guarantees that colocation provides. No more waiting for cloud GPU instances to become available.

Inference Providers and AI SaaS Companies

Companies serving AI inference at scale (image generation, LLM APIs, computer vision services) have predictable, high-utilisation GPU workloads that are extremely cost-sensitive. Colocation allows them to optimise their cost per inference request by owning the hardware and running it at high utilisation rates.

Enterprises with Data Sovereignty Requirements

Organisations in regulated industries (finance, healthcare, defence) that need to maintain physical control over their hardware and data often find colocation preferable to multi-tenant cloud environments. Colocation gives you a known physical location and dedicated hardware, which simplifies compliance with data residency and security requirements.

Key Requirements for GPU Colocation

GPU workloads are demanding. Before committing to a colocation provider, ensure they can deliver on these non-negotiable requirements:

Power: 30-50kW+ Per Rack

A fully populated GPU rack running NVIDIA H100 SXM servers draws roughly 40kW. Next-generation hardware like the GB200 NVL72 pushes even higher. Your provider must deliver this power with redundancy (2N or N+1 power distribution) and guarantee your allocated power under contract. Ask about their upstream utility connections and on-site generation capacity.

Liquid Cooling Infrastructure

Air cooling reaches its practical limits around 25-30kW per rack. Beyond that, you need direct liquid cooling (DLC) with in-row or in-rack coolant distribution, rear-door heat exchangers, or full immersion cooling. Verify that the provider's cooling infrastructure supports your specific server form factor and cooling requirements. Not all liquid cooling solutions are compatible with all GPU servers.

High-Bandwidth Interconnects

For multi-node training workloads, GPU-to-GPU communication bandwidth is critical. InfiniBand (400Gbps NDR or 800Gbps XDR) is the gold standard for training clusters. RDMA over Converged Ethernet (RoCE) is an alternative for some workloads. Your provider must support the cabling infrastructure and potentially the switch fabric that your cluster requires.

Network Connectivity

Diverse fibre paths, carrier neutrality, and low-latency access to cloud on-ramps and internet exchanges are essential. For inference serving, proximity to end users matters. For data pipeline workloads, high-throughput connections to cloud storage and data sources are key.

Physical Security and Compliance

Your GPU hardware represents a significant capital investment. Expect multi-factor biometric access controls, 24/7 CCTV, mantrap entry systems, and audit trails for all physical access. If you operate in a regulated sector, confirm the provider holds relevant certifications (ISO 27001, SOC 2 Type II).

GPU Colocation vs Cloud: A Cost Comparison

The cost argument for GPU colocation versus cloud GPU instances is straightforward at scale. Here is a realistic comparison for a single NVIDIA H100 GPU:

The colocation cost advantage grows with scale. At 100+ GPUs running 24/7, the savings over cloud can exceed $1 million per year. However, colocation requires upfront capital for hardware, operational maturity to manage the infrastructure, and a commitment to a specific location and contract term.

Cloud remains the better option for burst workloads, experimentation, and teams without infrastructure engineering capabilities. Many organisations run a hybrid approach: steady-state workloads on colocated hardware and burst capacity in the cloud.

How to Choose a GPU Colocation Provider

Choosing the wrong provider is expensive and disruptive. These are the criteria that matter most:

• Power availability and density: Can they deliver 30kW+ per rack today, not in six months? Is there room to scale to additional racks?
• Cooling technology: Do they have operational liquid cooling infrastructure, or is it "coming soon"? Ask for a site visit.
• Location: Proximity to your team, low-latency network paths to your users, and access to multiple network carriers. In the UK, London and the Thames Valley corridor offer the densest connectivity.
• Contract flexibility: Can you scale up within your contract term? What are the penalties for early termination? Are there price escalation clauses?
• Track record with GPU workloads: Have they successfully hosted high-density GPU deployments before? Ask for references from AI or HPC customers.
• Remote hands and support: What is the response time for physical interventions? Is 24/7 support included or charged per incident?
• Financial stability: Colocation is a long-term relationship. Evaluate the provider's financial health and the data centre owner's capital reserves.

Working with a specialist broker like ColoGPU can shortcut this process. We maintain a live database of verified providers with confirmed high-density availability across the UK, so you do not waste weeks chasing leads at facilities that cannot actually support your requirements.

Frequently Asked Questions