AI and GPU Colocation
High Density Racks, Liquid Cooling and the Data Centers That Can Actually Take Them
Most available data center space can't run rack-scale AI. Tell us the density, power and timeline, and the person who will run your search replies within 24 hours. No cost to you.
Check My Density Requirement- 35 to 230 kW per rack
- Air-cooled or liquid-cooled
- North American coverage
- Paid by the operator
Updated September 2026
GPU colocation is placing GPU servers you own in a data center that can power and cool them. The hard part is density: current rack-scale systems draw about 120 to 230 kW per rack and need direct-to-chip liquid cooling, while most data center space was built for 5 to 10 kW. Most available space cannot run them, and the gap widens with every hardware generation.
The AI Rack Density Ladder
Power per rack by NVIDIA platform, against the cooling ceilings a facility has to clear
Everything above Hopper sits past both cooling ceilings. A hall built for air cooling cannot serve any current rack-scale system, and a colocation lease signed today will run through at least two more generations.
Upper end of each range shown; bars to scale, kW per rack. Shipping figures from NVIDIA platform announcements and published rack specifications; Rubin Ultra and Feynman figures are industry projections and may change. Source: Metro Colo Advisory, AI Rack Density Ladder, September 2026. Free to cite with a link to this page.
The question is not which provider, but which specific hall can serve your density today and still serve you in year four, when the hardware has moved twice. Renting instead of owning? Our GPU rental guide covers reserved GPU servers and clusters; we place both and will tell you which costs less for your workload.
Send the density per rack, total power, cooling type and date. You'll hear back within 24 hours from the person who will run the search, with the next steps.
When Owning GPUs Beats Renting Them
Cloud GPU makes sense while models and usage are still changing. The math turns once a workload runs steadily around the clock, because on-demand pricing charges for flexibility you are no longer using. How much owning saves depends on what you compare it to, so here is the same fleet priced four ways.
Sources: CloudZero (AWS H100, August 2026); GetDeploying median, September 2026. Owned: eight servers at about $285,000 each over three years plus about $16,000 a month of colocation, a Metro Colo Advisory calculation from published prices. Illustrative, not a quote.
Than a hyperscaler on demand
The biggest gap, and the one most teams still running production inference on AWS or Azure are paying.
Of a GPU cloud on demand
Specialist GPU clouds already cut the hyperscaler price roughly in half. Owning halves it again for steady work.
Than a 12-month reservation
Closer than most people expect, before power and financing. Owning wins on fleets planned for three years or more, and you keep the hardware.
The middle ground: for a workload you will run for a year, reserving often beats buying once you count power, financing and the effort of running hardware. For fleets planned beyond three years, owning in colocation is usually the lower number. Current rental prices for every GPU are in our GPU rental guide, and the full ownership math is on the data center cost page. We price both paths at no cost.
What High Density Colocation Means in 2026
High density colocation has been redefined by AI, and the definition is still moving. The 5 to 10 kW per rack most facilities were designed around is not merely tight for GPU infrastructure; it is off by more than an order of magnitude. The platforms below set what a facility has to deliver to a single rack.
| Platform | Power per rack | Approximate weight | Cooling | Status |
|---|---|---|---|---|
| HopperHGX H100 | About 40 kW | Not published | Air or liquid | Shipping |
| BlackwellGB200 NVL72 | About 120 to 130 kW | 1,360 kg (about 3,000 lb) | Direct-to-chip liquid | Shipping |
| Blackwell UltraGB300 NVL72 | About 120 to 140 kW | 1,360 kg (about 3,000 lb) | Direct-to-chip liquid | Shipping |
| Vera RubinVR200 NVL72 | 190 to 230 kW | Not published | Direct-to-chip liquid | Volume shipping second half 2026 |
| Rubin UltraKyber NVL576 | About 600 kW | Not published | Direct-to-chip liquid, Kyber rack | 2027 |
| FeynmanRack not yet specified | Up to 1 MW, projected | Not published | Liquid | 2028 |
Weights approximate and vary by configuration. Figures for systems not yet shipping reflect published roadmap guidance and industry projections, and may change. Naming note: the Vera Rubin rack was first announced as NVL144, counting GPU dies; it is now the VR200 NVL72, counting 72 two-die GPU packages. Same system.
Two things follow. A hall built for 8 to 15 kW a rack cannot serve any of these systems without conversion, and the gap widens each generation. And a colocation lease signed in 2027 will typically run five to ten years, so the building has to accommodate hardware that has not been specified yet. That is a design question, decided before the slab is poured.
Most colocation facilities were built before AI workloads existed. The legacy carrier hotels in every major metro, 60 Hudson Street in New York, 350 East Cermak in Chicago and One Wilshire in Los Angeles, were designed for 3 to 5 kW per rack. Retrofitting for density is not the same as building for it.
What makes a data center AI ready
"AI ready" has become a marketing phrase. Four things separate a facility built for AI from one that supports a few high density cabinets:
Sustained, contiguous power
Density per cabinet held continuously, not as a peak, and available across a block of racks rather than in isolated positions.
Water to the white space
A facility water loop that reaches the data hall today, not one that is planned. Building it later is a construction project.
Floors rated for the weight
Roughly 1.5 tons per rack, which rules out many upper floors in converted buildings.
Electrical headroom
A distribution path with room for the next hardware generation, not just the current one.
AI data center design increasingly starts from the cooling loop and works outward, the reverse of how conventional facilities were planned. That is why a shortlist of AI colocation providers looks nothing like a shortlist of general colocation providers in the same market. Metro Colo Advisory screens facilities against all four at no cost.
Data Center Liquid Cooling, Compared
Density is a cooling problem before it is a power problem. Any facility can deliver more power to a rack; removing the heat is what limits what a hall can support, and it is where most facility evaluations go wrong.
| Method | Practical ceiling | What the facility needs | Serves rack-scale AI? |
|---|---|---|---|
| Air coolingHot and cold aisle containment | About 30 to 35 kW per rack | Standard containment | No |
| Rear door heat exchangerLiquid radiator on the rack door | About 50 to 70 kW per rack | Facility water to the rack door | No |
| Direct-to-chip liquidCold plates on the processors | 200 kW and above per rack | Water loop to the white space, plus a coolant distribution unit | Yes, required |
| ImmersionHardware submerged in dielectric fluid | Highest of any method | Tanks and different hardware handling | Not in current rack-scale form |
Is the loop there today?
Whether facility water reaches the white space now, or has to be built, is the single largest driver of whether a deployment date is achievable. Retrofitting a liquid cooling system into an operating hall is construction, not provisioning.
How warm can the water be?
Many rack-scale systems tolerate warm supply water, in some cases up to 45°C. That often allows free cooling instead of mechanical chilling, which widens the pool of qualifying facilities and lowers operating cost.
Who supplies the CDU?
The coolant distribution unit sits between the facility loop and the rack loop. Some racks ship with it built in; others need the facility to provide it. The answer changes capital cost and who is responsible when it fails.
A provider saying it supports liquid cooling and a specific hall having a loop to the white space today are different answers to the same question. Metro Colo Advisory verifies cooling capability at the facility level rather than the provider level, at no cost.
Power Architecture and the 800 VDC Transition
Cooling gets most of the attention in high density discussions. Power distribution is the constraint arriving behind it, and it will reshape which facilities can serve rack-scale deployments after 2027.
Traditional data centers distribute alternating current to the rack and convert it inside, with in-rack distribution typically at 54 volts DC. That works at kilowatt scale and breaks down as racks approach a megawatt: a 54-volt system at 1 MW per rack needs up to 200 kilograms of copper busbar per rack, takes space that could hold compute, and loses energy to conversion at every stage.
The industry answer is 800-volt DC distribution from the facility to the rack. NVIDIA has published a reference architecture for it, and Vertiv, Schneider Electric, Eaton and Delta have all announced commercial 800 VDC products for the second half of 2026, timed to the Rubin Ultra platform. Foxconn's 40 MW facility in Kaohsiung already runs on it, and CoreWeave, Lambda, Nebius, Oracle Cloud Infrastructure and Together AI are designing facilities around it.
What this means for a colocation buyer
For deployments shipping today, 800 VDC is not a requirement. Current Blackwell and Vera Rubin racks work with conventional distribution. It becomes a question where a lease term crosses the transition: an operator with no 800 VDC path is an operator whose facility has a ceiling, and a tenant signing a seven-year term in 2026 will be inside that facility when the ceiling matters. High-voltage DC also has different arc flash and fault characteristics than AC, so operators moving to it need updated certifications, procedures and trained staff before commissioning. That is a real timeline, and it is worth asking where an operator is in it rather than whether it intends to get there.
Four questions for any operator on a long-term high density deployment
1. What is the maximum sustained density this hall supports today?
This specific hall, not the portfolio. Sustained, not peak.
2. What is the documented path to higher density here?
What it would take, what it would cost, and when.
3. Is there an 800 VDC roadmap, and what is the timeline?
Including certifications and staff, not just equipment.
4. What density escalation rights would appear in our agreement?
A right to future capacity, in writing, not a verbal assurance.
Metro Colo Advisory asks these on behalf of clients and gets facility-level answers rather than marketing ones, at no cost.
A Market Examined Closely: High Density Options in the NYC Metro
The NYC metro is worth walking through because it shows a pattern that repeats in every market: many facilities, a small number that can serve density, and real differences between the ones that can. Most NYC colocation facilities cannot accommodate serious GPU deployments. Three purpose-built options can.
DataBank LGA3, Orangeburg NY
Best for mid-market AI density and value. Purpose-built for high density rather than retrofitted: air-cooled to about 35 kW per rack, liquid-cooled to 100 kW and above, with direct-to-chip, rear-door and immersion options. One-hop connectivity to DataBank's Manhattan sites at 111 8th Avenue and 60 Hudson Street, and the strongest HIPAA posture in its network, which makes it the call for healthcare AI. DataBank guide
CoreSite NY3, Secaucus NJ
Best for hybrid cloud AI. Completed in September 2025 with more than 138,000 square feet of purpose-built high density capacity next to the NY2 campus, 80+ networks and subsea connectivity. The differentiator is the Open Cloud Exchange: private connections to AWS, Azure, Google Cloud, Oracle and IBM with no public internet and no egress fees. Liquid-cooled deployments to about 40 kW per rack. CoreSite guide
Equinix NY5, Secaucus NJ
Best for financial services AI. High density capability within reach of the most concentrated financial data infrastructure in the world: cross-connects to the data providers, prime brokers, market makers and exchange infrastructure on the NY4 campus. Premium pricing is real; for trading-adjacent AI where latency to market data is a hard requirement, it is worth it. Equinix guide
| Facility | Best for | Density support | Pricing tier | Ecosystem strength |
|---|---|---|---|---|
| DataBank LGA3 | Mid-market AI, healthcare AI, value-focused density | 35 kW air, 100 kW+ liquid | Mid-range | One hop to the Manhattan carrier hotels; strongest HIPAA posture |
| CoreSite NY3 | Hybrid cloud AI, training and inference with cloud dependencies | 40 kW with liquid cooling | Mid-premium | 80+ networks; Open Cloud Exchange to AWS, Azure and Google Cloud without egress |
| Equinix NY5 | Financial services AI, quantitative models, trading-adjacent infrastructure | 50 kW with liquid cooling | Premium | Direct NY4 ecosystem access; financial data infrastructure proximity |
There is no single best facility for AI workloads. DataBank LGA3 wins on value and HIPAA, CoreSite NY3 on cloud connectivity, Equinix NY5 on the financial ecosystem. The right answer depends on the workload.
Send the density per rack, total power, cooling type and date. You'll hear back within 24 hours from the person who will run the search.
The Math: Cloud GPU Versus Dedicated Infrastructure
We do not publish exact contract pricing, because hardware costs and colocation rates move with market conditions. What we can show is the shape of the comparison, and it is consistent across client conversations.
Cloud GPU: what you are paying for
Compute, memory, networking, storage and egress, with the provider's margin on all of it. Reserved instances lower the hourly rate but lock you into a multi-year commitment with no hardware at the end. You are renting indefinitely at rates set by a provider whose incentive is to keep you renting.
Dedicated colocation: what you pay
Power and space in a professional facility, and the hardware you own. Your costs are fixed regardless of how many requests you serve, so cost per inference falls every month as utilization grows. At the end of a three-year contract you still own hardware with residual value.
The crossover point is lower than most teams expect
Most companies discover, when they run the numbers with someone who sees real colocation pricing daily, that the economics favor dedicated infrastructure well before they thought they would. Published rate cards are not what serious clients pay, and negotiated pricing for high density AI deployments looks very different from a provider website. That gap is what Metro Colo Advisory closes, at no cost to you.
For companies considering cloud repatriation of stable GPU workloads, the comparison earlier on this page shows the size of the gap: owning in colocation runs about 75 percent below hyperscaler on-demand pricing, about half the cost of a specialist GPU cloud, and about 25 percent below a 12-month reservation, before power and financing.
Why Dedicated Infrastructure Wins for Stable Inference
Predictable cost at any scale
Fixed costs stay fixed. As inference volume grows, cost per request falls. Cloud GPU does the opposite: the bill grows with every request, every month.
Performance you can count on
Shared cloud GPUs mean competing with thousands of other tenants. Dedicated hardware means your latency is yours alone: no noisy neighbors, no throttling at peak.
Data sovereignty and compliance
Your models, training data and outputs live on hardware you control, in a facility with documented physical security and certifications. For financial services, healthcare and legal, that is increasingly a requirement, not a preference.
No egress fees
Cloud egress on model outputs and data transfer is a material, growing cost at scale. In a carrier-neutral data center with 100 or more networks, you negotiate bandwidth directly. The economics are not slightly better; they are different.
You own something at the end
After a three-year contract you still own hardware with residual value. Cloud GPU leaves you with the option to renew at whatever price the provider sets.
The Hybrid Architecture: The Right Answer for Most AI Companies
Dedicated infrastructure is not the right answer for every GPU workload. For most AI companies the answer is hybrid: dedicated for stable production, cloud for everything that needs flexibility. Getting the split right is where most of the value is.
Move to dedicated infrastructure
Stable inference running at consistent utilization around the clock. Production models that are validated and not changing often. High-volume inference where cost per request is a business metric. Training on a predictable schedule. Workloads with compliance or data residency requirements that cloud complicates.
Keep on cloud
Model development and experimentation where requirements change weekly. Burst capacity for unpredictable spikes. Early-stage models not yet in stable production. Global distribution across many regions at once.
The companies getting this right are not abandoning cloud. They are making rational decisions about which workloads belong where, cutting the cost of their stable workloads by roughly half or more, and keeping cloud flexibility exactly where they need it. We help companies design this architecture and find the right dedicated infrastructure for the workloads that belong off cloud, at no cost.
North American Coverage for High Density Colocation
AI infrastructure decisions increasingly span several markets. We advise across the North American markets where serious AI deployments happen, and model the decision across them for every client whose workload does not require a specific metro.
Northern Virginia and Ashburn
The largest data center market in the world, with the deepest concentration of hyperscaler infrastructure, cloud on-ramps and AI-ready power. Best for AI workloads needing direct cloud connectivity at scale.
Dallas and Plano
Strong central position with growing high density capacity and better economics than the coasts. A fit for inference serving the central and southern US.
Phoenix
A fast-expanding high density hub with strong power infrastructure. A lower-cost alternative to the coasts for training.
Atlanta
Now leads all primary markets in construction, with growing high density capacity. A fit for inference serving the Southeast.
Chicago
Established high density infrastructure in the central US. A cost-effective option for Midwest-serving workloads.
Los Angeles and the Bay Area
Premium pricing reflecting West Coast tech proximity. Best for workloads that need to sit near West Coast companies and customers.
Quebec and Alberta
Power well below US primary market rates and free cooling much of the year. Best for training with no US latency requirement.
The right answer is often a hybrid spanning a primary market for ecosystem access and a lower-cost market for bulk training capacity.
What the Capacity Market Looks Like Right Now
High density capacity is the tightest part of an already tight market, and the constraint has moved from price to availability. CBRE's mid-2026 figures for the eight primary North American markets tell the story in three numbers.
Source: CBRE, North America Data Center Trends H1 2026.
Pricing has moved with it. Asking rates for 250 to 500 kW requirements rose 4.3 percent in the first half of 2026, nearly double the pace of a year earlier, and rates for deployments above 10 MW rose 6.7 percent. Volume discounts for large tenants have been reduced or eliminated in the most constrained markets; the old assumption that buying at scale earns a lower rate per kilowatt no longer reliably holds.
What that means for anyone planning a high density deployment
Contiguous space is the constraint, not total capacity
Occupiers struggle to secure 5 to 10 MW in a single block even where a market has capacity in aggregate. Even projects delivering through the end of 2027 have less than 10 percent vacancy.
New capacity takes two to three years
Power delivery timelines for new builds now run that long at a minimum. Near-term availability comes from existing inventory, not from anything built to order.
Requirements that can flex win
A deployment that tolerates warm supply water, or that can split across a campus rather than demanding one contiguous hall, qualifies at facilities a rigid requirement would be turned away from. Those two specifications alone often decide whether a search has three candidates or none.
Metro Colo Advisory runs live availability searches across the operator base rather than working from published capacity figures, at no cost.
Where Rack-Scale Density Actually Exists
The NYC metro comparison above ends with a limitation: none of the three purpose-built options serve the roughly 120 to 230 kW per rack that current-generation rack-scale platforms require. So where does that capacity exist?
The supply picture
Industry survey data puts average rack density at roughly 27 kW in 2026, up from about 16 the year before, and only one operator in five reports being ready for the 50 to 70 kW racks now routine in AI deployments. Rack-scale systems sit two to four times above even that. Facilities capable of 30 kW per rack and above are in short supply in Northern Virginia, Silicon Valley and Chicago specifically, the three markets where demand concentrates. The shortage is not evenly distributed, and neither is the solution.
The primary markets
Northern Virginia holds the deepest concentration of capacity capable of serious density. Equinix and Digital Realty have both announced high density expansions in the corridor, QTS is scaling hyperscale campuses in Ashburn and Richmond, and Sabey's Ashburn campus is designed for more than 100 kW per rack with liquid cooling, backed by a 300 MW substation on site. Silicon Valley carries the other concentration: Colovore in Santa Clara is the clearest example of a facility built for density rather than adapted to it, supporting 5 to more than 600 kW per rack with liquid cooling as the design premise, and extending the model to Chicago and Austin. The trade-off in both markets is time. Liquid-cooled deployments in Ashburn and Santa Clara commonly run three to six months from decision to live capacity, and the power queues behind them run longer.
The secondary markets
Phoenix, Columbus, Salt Lake City, Charlotte, Atlanta and Reno bring dense capacity online faster, because power is available and fewer tenants compete for it. Comparable liquid-cooled space in these markets often comes online in four to eight weeks rather than three to six months, and can price 20 to 40 percent below Northern Virginia. For any deployment without a hard latency or ecosystem requirement tying it to a coast, the secondary markets are usually the faster and cheaper answer, and the gap has widened over the past year.
Canada
Canada is absent from most US buyer guides and should not be. Quebec runs on hydro at rates well below US primary markets, with free cooling much of the year, which is exactly what makes a warm-water-tolerant deployment cheap to operate. QScale's Q01 campus near Quebec City publishes a specification almost nothing in the US matches: 142 MW secured, liquid-cooled cabinets rated well past 200 kW, floors reinforced for rack-scale weight, and free cooling about 80 percent of the year. eStruxture, the largest Canadian-owned platform, publishes 150 kW and above per cabinet in Vancouver and runs a 90 MW campus near Calgary. The trade-offs are real: cross-border data residency has to be settled first, latency to US population centers matters for inference (not training), the provider set is smaller, and availability at this density is as tight as anywhere. For training with no latency requirement, Canadian capacity is worth checking before assuming the answer is in Ashburn.
The minimum commitment problem
This is the constraint that surprises buyers most, and it has nothing to do with density. Tier 1 operators in the most constrained markets have begun gating new customers behind 100 kW or multi-megawatt commitments; a five to fifty kilowatt deployment that was a routine sale two years ago now gets queued or declined. Commitment size is not the only filter. Operators serving high density frequently raise the buyer's credit position before they discuss availability at all: at this tier, whether a requirement places is as much a question of who stands behind it as what it needs. And operators allocate scarce capacity toward tenants who buy more than power; a requirement that brings interconnection revenue is a different proposition from one that brings power alone. Mid-tier and regional operators, and operators outside the most constrained markets, still take deployments at that scale, and organizations below the Tier 1 threshold are often better served looking there first.
What the search actually looks like
Most rack-scale capacity trades off-market. Published availability lags what operators will actually commit to, and the capacity that fits a rack-scale requirement is often allocated in conversations that never appear in a listing. Operators with real density capability frequently answer by pointing at a project rather than a building, with dates that cluster in the following year and tend to slip. Available capacity, deployable capacity and capacity you can occupy on a given date are three different numbers, and only the first gets published. And the answer is frequently no: in recent searches for large liquid-cooled requirements at rack-scale density, the majority of the major North American operators approached declined, most citing no availability at that density now or in their forecast. That is why requirements that can flex on specification or geography place far more easily than requirements that cannot.
Metro Colo Advisory runs live availability searches across the operator base for rack-scale requirements at no cost, including operators outside our channel where the requirement demands it.
Common Mistakes in High Density Colocation Decisions
1. Choosing a carrier hotel for brand recognition
60 Hudson Street, 350 East Cermak and One Wilshire are world-class for enterprise infrastructure and wrong for serious AI density. Companies that pick them on familiarity often find they cannot deploy what they need.
2. Underestimating cooling
Air cooling tops out around 30 to 35 kW per rack whatever the facility quality. Anything above that needs liquid cooling, and companies that don't model it early hit facility constraints late.
3. Not modeling cross-connects for hybrid
Private connectivity to AWS, Azure and Google Cloud adds recurring cost. It usually saves far more in egress, but it belongs in the model.
4. Treating migration like cloud provisioning
Cloud GPUs spin up in minutes. Dedicated infrastructure runs 60 to 120 days from signing to operation. Plan for it, with the migration guide.
5. Signing standard contract terms
Standard colocation contracts assume standard density. High density deployments need power capacity guarantees, cooling service levels and density escalation rights, or there is no recourse when needs grow.
The Independent Advisory Approach to AI Infrastructure
High density decisions benefit from independent advice more than most: the capability gap between purpose-built and retrofitted facilities, the variance between published rates and what serious clients pay, the trade-offs between dedicated, cloud and hybrid, and a vendor sales motion that pushes its own facility regardless of fit.
Think of Metro Colo Advisory as a buyer's agent. We work for the client, not the provider. The provider you choose pays our commission, only when a deal closes, so there is no cost to you at any stage. We have no financial stake in which provider or facility you pick, and we hold channel relationships with the major operators, including Digital Realty, Equinix, DataBank, CoreSite and Cologix. Our only incentive is placing you in the right facility for the workload.
Frequently Asked Questions About High Density Colocation and AI Infrastructure
What is high density colocation?
High density colocation refers to data center deployments operating at 25 kilowatts per rack or higher, typically driven by GPU infrastructure, AI workloads, or accelerated computing requirements. Modern AI deployments routinely run at 30-100+ kilowatts per rack, requiring specialized facility infrastructure including advanced cooling, increased power density per cabinet, and engineered airflow management. Most legacy colocation facilities support only 5-10 kilowatts per rack and cannot accommodate modern AI infrastructure. Metro Colo Advisory identifies which facilities can physically accommodate your specific high density requirements at no cost.
Which facilities can actually serve rack-scale AI density?
Facilities capable of 140 kilowatts per rack and above with direct to chip liquid cooling are concentrated in Northern Virginia and Silicon Valley, with faster availability and better pricing in Phoenix, Columbus, Atlanta and Reno. Sabey’s Ashburn campus is designed to support over 100 kilowatts per rack with liquid cooling. Colovore in Santa Clara supports a range from 5 to over 600 kilowatts per rack. In Canada, QScale’s Q01 campus near Quebec City publishes liquid-cooled cabinets rated well past 200 kilowatts, and eStruxture publishes 150 kilowatts and above per cabinet in Vancouver. In the NYC metro, none of the purpose-built options currently advertise support at that tier. Metro Colo Advisory runs live availability searches across the operator base at no cost.
How much does high density colocation cost compared to cloud GPU?
Dedicated high density colocation typically reduces total GPU infrastructure cost by 40 to 65 percent compared to equivalent cloud GPU deployments for stable workloads running at consistent utilization. The cost reduction comes from eliminating cloud provider margins on compute, eliminating egress fees, and converting variable consumption pricing to fixed infrastructure pricing. Cloud GPU remains the right answer for genuinely variable workloads and active development. Metro Colo Advisory models cloud versus dedicated economics for your specific workload at no cost.
What is the difference between dedicated GPU servers and high density colocation?
Dedicated GPU servers refer to the hardware deployment configuration (GPU-equipped servers, typically 4-8 GPUs per server). High density colocation refers to the facility infrastructure that houses those servers (power, cooling, space). Companies pursuing dedicated GPU infrastructure need both elements — the right hardware configuration and the right facility capable of supporting that configuration’s power and cooling requirements. Most legacy facilities cannot accommodate serious GPU server deployments due to density constraints. Metro Colo Advisory evaluates both the hardware and facility requirements at no cost.
Can any colocation facility support liquid cooling?
No. Liquid cooling requires facility infrastructure that most colocation facilities do not have. Direct to chip cooling, rear door heat exchangers and immersion each require different capabilities. The question that decides it is whether a facility water loop reaches the white space today or has to be built, because retrofitting one into an operating hall is a construction project rather than a provisioning task. A provider saying they support liquid cooling and a specific hall having a loop to the white space are different answers to the same question. Metro Colo Advisory verifies cooling capability at the facility level rather than the provider level, at no cost.
Is dedicated infrastructure worth it for AI workloads under $20,000 monthly cloud spend?
Generally no. Below the $20,000 monthly cloud GPU spend threshold, the operational complexity of dedicated infrastructure typically outweighs the cost savings. Hardware procurement, facility contracts, capacity planning, and infrastructure operations require team capacity that smaller deployments don’t justify. Companies under this threshold are typically better served staying on cloud GPU until workloads stabilize and spend scales. Metro Colo Advisory tells you when your situation doesn’t yet justify dedicated infrastructure at no cost.
Where is AI colocation cheapest?
Outside the coastal markets. Phoenix, Columbus, Atlanta, Reno and Dallas typically deliver 20 to 30 percent better economics than Northern Virginia, Silicon Valley or the NYC metro for AI workloads that do not require proximity to those markets, and Quebec runs lower still on hydro power for training workloads with no US latency requirement. Within the NYC metro, DataBank LGA3 in Orangeburg delivers the strongest economics for mid-market AI deployments. Metro Colo Advisory models cost-effective AI infrastructure options across markets at no cost.
How long does it take to deploy dedicated AI infrastructure?
Typical deployment timelines run 60 to 120 days from contract signing to operational deployment, depending on facility readiness, hardware lead times, and integration complexity. Hardware procurement (especially for current-generation GPUs with constrained supply chains) is frequently the longest critical path element. Facility cross-connects and power provisioning typically run 30-60 days from contract execution. Companies planning data center migration from cloud to dedicated infrastructure should plan minimum 90-day timelines for production-grade deployments. Metro Colo Advisory provides realistic deployment timeline estimates for your specific situation at no cost.
What compliance certifications matter for AI colocation?
AI workload compliance requirements depend on the specific data being processed. Healthcare AI workloads typically require HIPAA BAA agreements and SOC 2 Type II certification. Financial services AI typically requires SOC 2 Type II, with additional requirements depending on specific regulatory regimes (SEC, FINRA, state banking regulations). Legal AI handling client data typically requires SOC 2 Type II and specific data residency provisions. DataBank LGA3 carries the strongest HIPAA posture among NYC high density facilities. Equinix NY4 and NY5 maintain comprehensive financial services compliance certifications. Metro Colo Advisory verifies compliance scope for your specific AI workload requirements at no cost.
What is the difference between air cooling, rear door heat exchangers, and direct to chip liquid cooling?
Air cooling has a hard physical ceiling around 30 to 35 kilowatts per rack regardless of facility quality. Rear door heat exchangers replace the back of the rack with a liquid-cooled radiator and extend the practical ceiling to roughly 50 to 70 kilowatts, which is why they appear in most liquid cooling retrofit projects. Direct to chip liquid cooling puts cold plates on the processors and runs a coolant loop into the rack itself, and it is required rather than optional for current rack-scale GPU systems. The question that decides whether a facility can serve a direct to chip deployment is whether a facility water loop reaches the white space today or has to be built, because retrofitting one into an operating hall is a construction project rather than a provisioning task. Metro Colo Advisory verifies cooling capability at the facility level at no cost.
How much power does a GB300 NVL72 or Vera Rubin rack need?
A GB300 NVL72 draws 140 to 160 kilowatts sustained and weighs roughly 1.5 tons. The Vera Rubin VR200 NVL72, shipping in volume from the second half of 2026, draws roughly 190 to 230 kilowatts. Rubin Ultra on the Kyber rack architecture is specified at around 600 kilowatts for 2027, with megawatt-class racks on the roadmap after it. None of these can be air cooled at any density; all require direct to chip liquid cooling. For comparison, most existing colocation facilities support 5 to 10 kilowatts per rack. Metro Colo Advisory verifies rack-scale capability facility by facility at no cost.
What should a high density colocation contract include that a standard one does not?
Colocation terms run five to ten years while the GPU hardware envelope has been changing every twelve months, so the provisions that matter most are the ones governing what happens as density requirements grow. A high density agreement should specify the maximum sustained rack density the specific hall supports rather than a portfolio figure, document the path and cost to increase density within that hall, address cooling service levels separately from power, and include density escalation rights that give the tenant an actual claim on future capacity. Standard colocation contracts assume standard density and contain none of this. Metro Colo Advisory reviews high density contract terms for clients at no cost.
Ready to Check Your AI Infrastructure Requirement?
The right answer depends on the workload, the density, the compliance posture, the cloud dependencies and the growth path. There is no single best facility for all AI workloads. Send us the density per rack, total power, cooling type and date, and you'll hear back within 24 hours from the person who will run the search. Metro Colo Advisory has no financial stake in which facility you choose, and the provider pays us, so it costs you nothing.
Density, power, cooling and date are enough to start.
Renting instead of owning: the GPU rental guide, CoreWeave competitors and bare metal.
Provider guides: Equinix, Digital Realty, DataBank, CoreSite, Cologix, and the full provider comparison.
Other markets: Atlanta, Chicago, Dallas, San Francisco, Washington DC, the NYC metro and Manhattan.
Buyer education: what colocation is, the pricing guide, data center tiers, contract terms, migration planning, cloud repatriation, hybrid cloud and the cloud vs colo calculator.
Specialized requirements: disaster recovery, HIPAA, financial services, the compliance guide, and data center consulting for developers and investors weighing build versus lease.
