GPU Colocation Providers Compared: Europe 2026

The European GPU colocation market is fragmented. Dozens of providers claim to support high-density AI workloads, but their capabilities vary enormously. Some facilities were purpose-built for GPU clusters with liquid cooling and 50kW+ racks from day one. Others are legacy enterprise data centres that have bolted on a few high-density suites as an afterthought. The difference matters when you are deploying millions of pounds worth of GPU hardware and need it running at full capacity from day one.

This guide compares the leading GPU colocation providers across Europe on the criteria that actually matter for AI workloads: power density, cooling infrastructure, connectivity, sustainability, and commercial flexibility. Whether you are planning a large-scale training cluster in the Nordics or a low-latency inference deployment in London, this comparison will help you shortlist the right providers for your requirements.

How We Compare Providers

We evaluate GPU colocation providers against eight core criteria, weighted towards the factors that most directly affect AI infrastructure performance and cost. Power density (maximum kW per rack) determines whether a facility can physically support modern GPU hardware. Cooling capability -- air, liquid-ready, or fully operational liquid cooling -- determines whether that power density is achievable in practice. Connectivity covers carrier diversity, internet exchange access, and cloud on-ramps. Certifications (ISO 27001, SOC 2, TUV) matter for enterprise and regulated workloads. Sustainability covers renewable energy sourcing and PUE. Pricing model and minimum commitment affect commercial flexibility. Finally, geographic coverage determines whether a provider can support multi-site deployment strategies. For a deeper look at what drives facility costs, see our GPU colocation pricing guide.

Provider Comparison at a Glance

The table below summarises the fifteen providers we most frequently encounter when sourcing GPU colocation capacity for clients across Europe. Maximum kW per rack figures reflect the highest density currently available or contractually supported -- not necessarily the default offering.

Power density figures are indicative and subject to change as providers expand capacity. Always confirm current availability directly or through a broker -- the gap between a provider's marketed capability and actual available inventory can be significant.

Best for Large-Scale AI Training

For training runs that consume hundreds or thousands of GPUs over weeks or months, three factors dominate provider selection: power cost, cooling capacity, and sustained availability. The Nordics lead on all three. Verne Global in Iceland operates from a former NATO base with access to geothermal and hydroelectric power at some of the lowest rates in Europe. Their facility supports power densities exceeding 70kW per rack with a combination of natural cooling and direct liquid cooling infrastructure. For organisations training foundation models where the electricity bill is the single largest operating cost, Iceland's energy economics are difficult to match anywhere else on the continent.

Lefdal Mine Datacenter in Norway takes a different approach, operating inside a former mineral mine on the edge of Nordfjord. The facility uses fjord water for cooling, delivering a PUE below 1.15 and supporting rack densities of 50kW+. The mine's natural thermal mass and water supply provide inherent cooling redundancy. Green Mountain, also in Norway, runs entirely on hydroelectric power and offers similar sustainability credentials at slightly lower density points, making it an attractive option for organisations that need cost-effective green colocation without pushing to the extreme densities that Verne Global supports.

The trade-off with Nordic locations is latency to end users. Round-trip times from Iceland to London are approximately 30-40ms, and from Norway around 20-30ms. For training workloads, this is irrelevant -- models do not need to serve predictions in real time during training. But it means Nordic facilities are best suited to batch training and fine-tuning rather than production inference. The smart approach, which we see increasingly among clients, is to train in the Nordics and deploy inference models closer to users.

Best for Low-Latency AI Inference

Inference workloads have fundamentally different infrastructure requirements from training. Latency to end users matters. Network interconnection density matters. The ability to peer directly with cloud providers and content delivery networks matters. This is where facilities in London and Frankfurt dominate.

Kao Data in Harlow, just north of London, was designed from the ground up for high-performance computing. Their facility supports 30kW+ per rack with liquid cooling readiness and offers direct dark fibre connectivity to the London Internet Exchange (LINX) and major cloud regions. For AI companies serving UK and European end users, Kao Data provides the combination of density, connectivity, and purpose-built design that inference deployments demand. VIRTUS Data Centres, with multiple campuses across London, offer even higher density options at 50kW+ per rack, positioned firmly at the hyperscale and enterprise end of the market with advanced liquid cooling already operational.

Equinix remains the default choice for organisations that prioritise interconnection above all else. Their European footprint spans over 40 data centres across major markets, and their ecosystem of carriers, cloud on-ramps, and peering exchanges is unmatched. Power density has historically been a weakness -- Equinix facilities were designed for networking and enterprise IT rather than GPU clusters -- but their newer builds and retrofit programmes are pushing to 40kW+ per rack. For inference workloads where you need to connect to everything, Equinix is difficult to avoid. Similarly, cloudKleyer in Frankfurt offers direct access to DE-CIX, Europe's largest internet exchange, with 50kW+ rack density -- making it a strong option for inference serving into the German and Central European markets.

Best for Cost-Optimised GPU Hosting

Not every AI workload requires a Tier IV facility with triple-redundant cooling loops. For many startups and mid-market AI companies, the priority is getting reliable GPU capacity at the lowest possible per-kilowatt cost. Hetzner in Germany has built a reputation on exactly this: no-frills, competitively priced colocation with solid reliability. Their facilities support 30kW+ per rack with air and liquid-ready cooling, and their pricing is consistently 20-30% below what the premium European operators charge for comparable power density. The trade-off is a more self-service operational model with less hand-holding.

In the Netherlands, Greenhouse Datacenters combines cost efficiency with sustainability. Their liquid-ready facilities support 50kW+ per rack and are designed around circular economy principles, with waste heat reuse and renewable energy sourcing. For AI companies that face sustainability reporting requirements from investors or customers, Greenhouse offers a way to keep costs competitive without compromising on environmental credentials. NorthC, operating across the Netherlands, Germany, and Switzerland, provides another cost-effective option with regional multi-site coverage -- useful for organisations that need to distribute workloads across jurisdictions without paying hyperscale prices.

The Nordic providers -- Verne Global, Lefdal Mine, and Green Mountain -- also compete strongly on cost for large deployments. Their energy prices are among the lowest in Europe, and the natural cooling available at their locations reduces the facility overhead that gets passed through to customers. The key distinction is that Nordic cost advantages are most pronounced at scale. A single rack in Iceland does not justify the logistical overhead of a remote deployment; a hundred racks absolutely does. For a broader look at how colocation compares to cloud on cost, see our dedicated analysis.

Best for Enterprise Compliance

Regulated industries -- financial services, healthcare, defence, and public sector -- require colocation providers with robust certification portfolios and demonstrable security practices. noris network in Germany holds TUV Level 4 certification, the highest tier of German data centre security accreditation, alongside ISO 27001 and SOC 2 Type II. Their facilities in Nuremberg and Munich are built to exacting standards and cater to organisations where audit readiness and regulatory compliance are non-negotiable. Equinix similarly holds comprehensive certifications across its European portfolio, including ISO 27001, SOC 2, PCI DSS, and various national security accreditations, making it a safe default for compliance-sensitive deployments.

CyrusOne, with enterprise-grade facilities across Europe, offers similarly strong compliance credentials and a track record of serving large financial institutions and government contractors. Their facilities are designed for redundancy and security, with multi-layered physical access controls and comprehensive audit trails. For organisations deploying GPU infrastructure under strict regulatory requirements, the slightly higher cost of these enterprise-focused providers is justified by the reduced compliance risk and the ability to satisfy auditors with established certification frameworks rather than bespoke security assessments.

Best for Hyperscale Deployments

When your deployment plan starts at 10MW and scales upward, the provider shortlist narrows to operators with campus-scale facilities and the capital to build ahead of demand. Vantage Data Centers operates hyperscale campuses across EMEA with the latest cooling technology and power infrastructure designed for the densities that next-generation GPU hardware demands. Their facilities support 50kW+ per rack and can accommodate multi-megawatt deployments under single contracts. STACK Infrastructure offers similar scale with a reputation for flexible commercial terms -- a significant consideration when you are negotiating a deployment worth tens of millions over multiple years.

DATA4 operates hyperscale campuses across France and Southern Europe, with advanced cooling infrastructure and the land bank to scale well beyond current capacity. Their multi-site presence is particularly relevant for organisations serving the French and Mediterranean markets. For pan-European hyperscale requirements, these operators can typically provide the combination of raw capacity, financial stability, and operational maturity that large-scale GPU deployments require. The key question at this scale is less about technical capability -- all of these operators can deliver the infrastructure -- and more about commercial terms, geographic alignment, and long-term partnership fit.

Key Factors When Comparing GPU Colocation

Power Density Is Not Everything

A provider advertising 50kW per rack does not mean every rack in their facility can deliver that. Many providers quote maximum density for their newest or purpose-built halls while the majority of their floor space operates at standard 10-15kW densities. Always ask how many high-density racks are available today, not how many could theoretically be provisioned. Get the answer in writing.

Cooling Method Determines Real Capacity

The cooling infrastructure, not the electrical feed, is typically the binding constraint on rack density. A facility may have the electrical capacity for 50kW per rack but lack the cooling throughput to remove that heat. Direct liquid cooling (DLC) is now the baseline for serious GPU deployments. Rear-door heat exchangers are a mid-range option. Full immersion cooling remains niche but is gaining traction for the highest densities. Verify that the provider's cooling solution is compatible with your specific server hardware -- not all DLC manifolds fit all chassis.

Contract Flexibility

GPU infrastructure needs change rapidly. A provider that locks you into rigid three-year terms with no scaling provisions or early-exit clauses may cost you more in lost flexibility than they save in per-kilowatt pricing. Look for contracts that allow you to add racks within an agreed ramp schedule, adjust power allocations as hardware generations change, and exit or transfer capacity if your strategy shifts. The best providers treat flexibility as a competitive advantage, not a risk.

Hidden Costs

The headline per-kW price is only part of the total cost. Cross-connect fees (connecting to carriers and cloud on-ramps) can add thousands per month. Smart hands charges for physical interventions vary from included-in-contract to eye-watering per-incident fees. Power overage penalties -- what happens when your draw exceeds your contracted allocation -- range from gentle usage-based billing to punitive surcharges. Model the full cost, not just the base rate.

Geographic Strategy

Where you place your GPU hardware should be a deliberate strategic decision, not a default to the nearest available facility. Training workloads belong where power is cheapest and cooling is most efficient: the Nordics, Iceland, or emerging green-energy markets. Inference workloads belong where your users are: London, Frankfurt, Amsterdam, Paris. Many of our clients operate a split-deployment model, with training clusters in low-cost locations and inference fleets in latency-optimised markets. A broker can help you design and source this kind of multi-site strategy.

Training vs Inference: Different Provider Priorities

The distinction between training and inference is the single most important factor in provider selection, yet it is frequently overlooked. Training workloads are batch-oriented, latency-insensitive, and overwhelmingly dominated by power cost. A training run on a thousand GPUs for three weeks will consume hundreds of megawatt-hours of electricity. At Nordic energy rates of EUR 0.03-0.05 per kWh versus Western European rates of EUR 0.15-0.25 per kWh, the cost difference on a single training run can reach six figures. This is why serious AI training is gravitating towards Iceland, Norway, and northern Sweden.

Inference workloads are the opposite: latency-sensitive, connection-dense, and geographically distributed. An inference API serving customers across Europe needs single-digit millisecond response times, which means placing GPU servers in London, Frankfurt, or Amsterdam -- close to internet exchanges, cloud on-ramps, and end users. The power cost premium of these locations is justified by the latency benefit. Smart AI companies are increasingly adopting this split: train in the Nordics where every kilowatt-hour costs a fraction of what it costs in London, then deploy the resulting models to edge-adjacent colocation in major European metros for production serving.

Frequently Asked Questions