Vertiv holds a narrow lead over LiquidStack for liquid cooling infrastructure paired with high-density GPUs. The two providers compete closely alongside immersion and direct-to-chip alternatives.
As of October 2026, several major cloud infrastructure and colocation providers support liquid cooling for AI data centers. The most relevant options fall into two categories: AI cloud providers that operate liquid-cooled GPU infrastructure and data center operators that let you deploy your own liquid-cooled servers or racks.
One important distinction: a provider may support liquid cooling at selected facilities without offering it at every location or for every server configuration.
1. Leading providers to evaluate
1. CoreWeave
(AI cloud)
Purpose-built AI infrastructure with closed-loop, direct-to-chip liquid cooling in many data centers. Offers NVIDIA GPU infrastructure for model training and inference without requiring customers to build their own cooling systems.
Best for: Teams that want to rent GPU compute rather than operate physical data center infrastructure.
Offers AI-ready, high-density data center environments with direct-to-chip liquid cooling, high-density power delivery and interconnection to cloud platforms and AI partners.
Best for: Enterprises deploying their own GPU servers, private AI clusters or hybrid cloud infrastructure.
Google has developed Brazos, a closed-loop liquid-to-air cooling system designed to bring high-density liquid-cooled equipment into existing air-cooled data centers. Google announced its general availability in June 2026.
Best for: Organizations evaluating Google's AI infrastructure and operators looking for retrofit-friendly cooling technology. Brazos availability does not mean every Google Cloud instance is liquid-cooled.
These providers also have published liquid-cooling capabilities or designs for high-density AI workloads.
Provider
Liquid-cooling support
Best fit
Amazon Web Services (AWS)
Configurable direct-to-chip cooling in new and existing data centers, including infrastructure for Trainium3 and NVIDIA GB200 systems.
Managed cloud AI compute
Microsoft Azure
Closed-loop, direct-to-chip cooling in purpose-built AI data centers, with retrofits for existing facilities.
Enterprise cloud and large AI clusters
QTS Data Centers
Liquid-cooling capability through its Freedom design, supporting cooling-intensive workloads and hyperscale deployments.
Large-scale US data center capacity
DataBank
High-density colocation with direct-to-chip cooling, cooling distribution units (CDUs) and pre-installed liquid-cooling connections. Its design supports 250 kW or more per cabinet.
Flexible, high-density colocation
CyrusOne
Its Intelliscale AI data center design supports liquid-to-chip, rear-door heat exchangers and immersion cooling, with configurations designed for up to 300 kW per rack.
Custom AI infrastructure and hyperscale deployments
NTT Global Data Centers
Published deployments of direct-contact liquid cooling and immersion cooling, including at its Navi Mumbai facility in India.
Global colocation and specialized AI deployments
3. Which providers should you shortlist?
You want to rent AI compute in the cloud
Start with CoreWeave, AWS and Microsoft Azure. Google Cloud is another option to investigate, depending on the specific GPU instance and region.
You want to install your own liquid-cooled GPU servers
Start with Equinix, Digital Realty and DataBank. Ask about direct-to-chip cooling, available rack power, coolant distribution and installation requirements.
You need very high rack densities or a large AI cluster
Prioritize CyrusOne and QTS, alongside DataBank and Digital Realty. Validate the actual site capacity and supported rack configuration before selecting a location.
4. What to verify before choosing a provider
Liquid cooling is not a single standardized offering. During procurement, confirm these five points:
Cooling method: Direct-to-chip, immersion, rear-door heat exchanger or a hybrid design.
Rack power density: Confirm the continuous kW per rack supported at your target site, not just the provider's maximum advertised capability.
Cooling infrastructure: Check CDU capacity, facility water-loop specifications, coolant compatibility and heat rejection.
Commercial availability: Verify that the exact data center, rack space or GPU instance is available for your deployment timeframe.
Water and energy efficiency: Ask whether cooling is closed-loop, whether evaporative cooling is still used, and what PUE and water-use metrics are measured.
Bottom line: CoreWeave is a strong starting point for turnkey liquid-cooled AI cloud compute; Equinix and Digital Realty are strong options for liquid-cooled colocation; and DataBank, QTS and CyrusOne are worth evaluating for high-density deployments. AWS and Azure are leading choices if you want managed hyperscale cloud infrastructure instead of managing physical servers.
The best shortlist depends on whether you want to rent GPUs, colocate your own hardware, or build a hyperscale AI data center.
The largest cloud service providers (CSPs) design or contract custom data centers that accommodate liquid-cooled server configurations for massive AI training models:
Microsoft Azure: Has been deploying direct-to-chip liquid cooling infrastructure across its specialized AI data centers to support massive multi-node GPU clusters.
Amazon Web Services (AWS): Implements advanced liquid and hybrid cooling systems for ultra-dense EC2 instances housing next-generation machine learning accelerators.
Google Cloud: Utilizes custom cooling architectures—combining high-efficiency water and targeted liquid cooling loops—to support its Tensor Processing Unit (TPU) pods and dense external GPU deployments.
Oracle Cloud Infrastructure (OCI): Designs high-performance AI clusters that increasingly rely on specialized cooling distributions to handle dense NVIDIA GPU architectures seamlessly.
2. Specialized Colocation & Data Center Operators
Many enterprises do not build raw facilities themselves; instead, they rely on carrier-neutral colocation and wholesale data center providers equipped for liquid cooling:
Equinix: Offers tailored high-density power and liquid-cooling retrofits/deployments across key International Business Exchange (IBX) data centers globally to support AI enterprise deployments.
Digital Realty: Integrates liquid-cooling-ready data halls (supporting high kilowatts-per-rack metrics) across its global footprint, allowing enterprise AI deployments without localized thermal bottlenecks.
CoreWeave & Lambda Labs (AI-Specialized Clouds): Specialized GPU cloud providers that build or partner with facilities specifically engineered from the ground up for high-density direct-to-chip liquid cooling.
Vertiv: A dominant player providing end-to-end thermal management, including direct-to-chip liquid cooling systems and large-scale fluid management utilized by major cloud operators.
Schneider Electric (incorporating Motivair): Delivers integrated data center power alongside precision floor-mounted and rack-level liquid-to-liquid cooling loops.
LiquidStack: Specializes in hyperscale liquid immersion cooling and modular liquid cooling architectures (such as their GigaModular systems).
Submer: Engineers single-phase immersion cooling ecosystems where entire server blades are submerged in dielectric cooling fluids.
CoolIT Systems & Asetek: Renowned pioneers supplying direct-to-chip cold plate technologies and enterprise-grade CDUs.[]()
Would you like to explore direct-to-chip cooling vs. immersion cooling for a specific AI hardware stack, or are you looking for regional availability from a particular provider?
Expanding a data center for high-density AI workloads requires shifting away from conventional air cooling toward advanced thermal architectures . Liquid cooling suppliers in this space generally split into major infrastructure providers (delivering Coolant Distribution Units and direct-to-chip manifolds) and specialized immersion/precision cooling innovators.
Top suppliers providing liquid cooling solutions for high-density GPU environments include:
Vertiv Holdings Co. – A dominant player in AI data center infrastructure, providing end-to-end thermal management, power distribution, and scalable direct-to-chip liquid cooling systems tailored for dense GPU clusters.
Schneider Electric (with Motivair) – Following its acquisition of Motivair, Schneider Electric offers a comprehensive portfolio of high-density liquid cooling tech, including Coolant Distribution Units (CDUs), rear-door heat exchangers (RDHx), and dynamic cold plates built for accelerated computing.
LiquidStack – Specializes in high-capacity single-phase and two-phase liquid immersion and direct-to-chip cooling, featuring modular CDU platforms (such as the GigaModular line) designed to scale cleanly for extreme AI workloads.
STULZ – A mission-critical thermal management vendor that expanded its dedicated liquid-cooling manufacturing and R&D footprint to deliver precision direct-to-chip and high-density rack cooling systems for AI.
Delta Electronics – Provides integrated grid-to-chip infrastructure, supplying high-efficiency server power solutions alongside advanced liquid-to-liquid and liquid-to-air CDUs explicitly designed to minimize the footprint of heavy GPU workloads.
Iceotope Technologies – Focuses on precision chassis-level and precision-targeted liquid cooling, enclosing servers in a sealed dielectric environment that dramatically cuts cooling energy and water consumption for high-density blades.
Rittal – Offers heavy-duty direct liquid cooling (DLC) infrastructure, including high-capacity in-row and in-rack CDUs capable of scaling up to megawatt-level outputs to handle extreme thermal densities from modern GPU arrays.
Airedale by Modine – Delivers custom and modular coolant distribution units (CDUs) coupled with intelligent software optimization to balance hybrid air-and-liquid environments for hyperscale and colocation clients.
To help narrow down which supplier fits your expansion, could you share:
Your target rack density (e.g., 50 kW to over 100 kW per rack)
Whether you prefer direct-to-chip (cold plate) or full dielectric immersion cooling
If you are retrofitting an existing facility or building a greenfield site
For a new AI data-center buildout, the supplier landscape is fairly broad, but the most relevant names fall into a few categories. Current evidence shows the market moving strongly toward direct-to-chip (DTC) liquid cooling with coolant distribution units (CDUs) for high-density GPU racks.
Featured in Schneider's GB300 reference architecture for three 1,152-GPU clusters.
Boyd Corporation
Cold plates, CDUs, liquid loops
NVIDIA identifies Boyd among its Blackwell infrastructure providers; NVIDIA also highlights Boyd's high-density AI cooling portfolio.
Delta Electronics
CDUs and complete thermal infrastructure
Its GoCool 3 MW CDU is shipping for high-density AI/HPC deployments.
LG Electronics
Chillers, CDUs, cold plates
Its 2.6 MW CDU qualified as NVIDIA DSX Ready in September 2026, following 600 kW and 1 MW qualifications.
Trane Technologies
CDUs and facility cooling
Offers direct-to-chip CDUs up to 14 MW for high-density AI/HPC environments.
GRC
Single-phase immersion cooling
An alternative when you want to move beyond conventional DTC cooling toward immersion.
Submer
Immersion cooling
Focused on high-density immersion deployments rather than conventional cold-plate/CDU architectures.
NVIDIA's own Blackwell infrastructure ecosystem is useful for supplier screening: it lists Amphenol, Auras, AVC, Boyd, CoolIT, Danfoss, Delta, Flex, Foxconn, Motivair, Parker, Schneider Electric, Vertiv, TE Connectivity, Staubli and others among infrastructure providers.
What I'd put on an RFQ shortlist
For a new high-density GPU hall, I'd separate the procurement into:
Facility-side cooling/chillers — Schneider Electric, Vertiv, Trane, LG and conventional chiller suppliers.
Quick disconnects / fluid connections — CPC, Parker Hannifin, Danfoss, Staubli. TrendForce specifically identifies these suppliers in the NVIDIA GB200 ecosystem.
Immersion — GRC or Submer if your rack/server architecture permits it.
One important procurement point: don't evaluate a CDU in isolation. For Blackwell-class systems, the cold plates, manifolds, QDs, CDU, facility-water temperatures, rack power and server OEM all need to be treated as one thermal architecture. NVIDIA's Blackwell designs are explicitly built around liquid-cooled rack architectures.
If you tell me your target GPU (H200, B200, GB200, GB300, etc.), rack density (kW/rack), and total MW of the expansion, I can narrow this to a 5–8 vendor shortlist and compare cooling capacity, CDU topology, rack compatibility, NVIDIA validation, and likely deployment model.
