United States AI Chip Cooling Market

Overview

The United States AI Chip Cooling Market was valued at USD 1.15 billion in 2025 and is projected to reach USD 5.60 billion by 2034, growing at a CAGR of 17.5% during the forecast period (2026-2034). The market is driven by rapidly escalating GPU thermal design power, accelerating hyperscale AI campus construction across the Southern and Western United States, and mounting pressure on data center operators to curb cooling-related energy and water consumption as AI training and inference workloads scale nationwide. The market is shifting from conventional air-cooled and hybrid rear-door architectures toward fully liquid-cooled rack designs engineered around specific GPU platforms such as NVIDIA's GB200 and GB300 systems. Cooling vendors are moving away from standalone component sales toward pre-validated, chip-to-facility reference architectures co-developed directly with silicon and server manufacturers, shortening deployment timelines for hyperscale operators. Government initiatives such as the Liquid Cooling for AI Act of 2025, introduced in the US Senate in November 2025 by Senators Dave McCormick and Chris Coons with bipartisan co-sponsorship, direct a federal technology assessment of liquid cooling research and development needs for AI compute clusters and call for evaluation of liquid cooling deployment across federal AI infrastructure. The legislation responds to Lawrence Berkeley National Laboratory projections that US data centers could consume 580 terawatt-hours of electricity by 2028, and is intended to support more energy-efficient, grid-friendly AI infrastructure buildout nationwide. The Southern United States, led by Virginia and Texas, held the largest share of the AI chip cooling market in 2025 owing to its dense concentration of hyperscale and colocation facilities. The Western United States, led by Arizona, Nevada, and Oregon AI campus construction, is projected to be the fastest-growing region through 2034.

Market Size & Share

Market Dynamics

KEY MARKET TREND

Shift Toward Two-Phase and Microfluidic Direct-to-Chip Cooling Gaining Momentum Among AI Chip Cooling Vendors

• As GPU thermal design power moves toward and beyond 4,000 watts per accelerator, single-phase cold plates are approaching practical limits for removing concentrated heat flux from modern AI silicon. Vendors are responding by commercializing two-phase direct-to-chip systems that use dielectric refrigerants boiling at the chip surface, enabling more uniform temperature control across densely packed GPU boards.
• Companies such as ZutaCore and Accelsius have engineered waterless, two-phase cold plates capable of cooling processors with thermal design power above 3,500 watts while eliminating thermal gradients that appear across large multi-die GPU packages in single-phase systems. This phase-change approach absorbs substantially more heat per unit of coolant than conventional liquid loops.
• Established liquid cooling suppliers and industrial investors are backing two-phase technology through direct equity investment, with Johnson Controls and Legrand participating in Accelsius funding rounds and Mitsubishi Electric, Carrier Ventures, and Samsung Ventures backing ZutaCore. This convergence of strategic and financial investors signals growing confidence in two-phase cooling as a mainstream complement to single-phase systems.
• ZutaCore closed a USD 100 million funding round in June 2026 backed by Mitsubishi Electric, Carrier Ventures, and Samsung Ventures to scale production of its HyperCool two-phase direct-to-chip cooling platform. The round followed a USD 65 million Series B raise by competitor Accelsius led by Johnson Controls, underscoring intensifying investment activity in two-phase cooling technology.

KEY MARKET DRIVER

Rising GPU Power Density and Rack Thermal Loads Driving Adoption of Liquid Cooling Across US AI Data Centers

• GPU thermal design power has climbed sharply across recent NVIDIA and AMD accelerator generations, with reference rack architectures for next-generation platforms such as NVIDIA's Rubin system reaching power densities near 225 kilowatts per rack. Conventional air-cooling systems cannot economically dissipate heat loads at this concentration, making liquid cooling a structural requirement for new AI data center construction.
• Data center operators building AI training and inference capacity face escalating pressure to control both capital and operating costs as rack densities rise, since liquid cooling can substantially reduce reliance on mechanical chillers and computer room air handlers compared with air-cooled designs. Lower cooling energy consumption also directly improves power usage effectiveness, a metric closely scrutinized by hyperscale customers and utilities.
• Component and system vendors are responding to this demand by rapidly expanding US manufacturing capacity, with liquid cooling suppliers adding hundreds of thousands of square feet of new production space across multiple states over the past two years to keep pace with hyperscaler procurement schedules tied to successive AI accelerator generations.
• According to Lawrence Berkeley National Laboratory's 2025 update to the U.S. Data Center Energy Usage Report, data centers could account for approximately 11.8% of total U.S. electricity consumption by 2030, with a scenario range of 9.5%–15.3%. The report's bottom-up assessment incorporates planned data-center IT-equipment shipments, per-device electricity consumption, and cooling-system performance, highlighting the growing energy burden associated with expanding U.S. data-center infrastructure and the increasing importance of cooling-system efficiency.

KEY MARKET OPPORTUNITY

Federal Policy Support for Energy-Efficient Liquid Cooling Creating New Growth Avenues

• Rising political attention to AI-driven electricity demand is creating an opening for cooling vendors to position liquid and immersion technologies as tools for easing strain on regional power grids. Federal and state policymakers are increasingly framing cooling efficiency as part of broader AI infrastructure and grid-reliability policy discussions rather than a purely operational data center concern.
• Vendors able to demonstrate measurable reductions in water and energy consumption stand to benefit as utilities in data-center-dense states begin weighing efficiency considerations in interconnection and permitting discussions for large AI campuses. This creates commercial opportunity for immersion and two-phase cooling suppliers whose technologies reduce or eliminate evaporative water use versus conventional chilled-water systems.
• Opportunities are also emerging in retrofitting the large base of existing US colocation and enterprise data centers originally designed for air cooling, as modular coolant distribution units and rear-door heat exchangers allow operators to add liquid cooling capacity incrementally without full facility redesign, opening an addressable market beyond new-build hyperscale campuses.
• The Liquid Cooling for AI Act of 2025, introduced by Senators Dave McCormick and Chris Coons with bipartisan support in November 2025, directs a federal technology assessment of liquid cooling research and development needs for AI compute clusters, creating a formal government evaluation process that cooling vendors can engage with to support future federal procurement and grid-reliability policy.

United States AI Chip Cooling Market Size, 2025-2034 (USD Billion)

Segmentation Analysis

Analysis by Type (Cooling Technology)

Single-phase direct-to-chip liquid cooling captured the largest share of the United States AI chip cooling market in 2025, reflecting its status as the default retrofit-friendly architecture for hyperscale and colocation operators moving away from air-cooled racks. Cold plates mounted directly on GPUs and CPUs remove the majority of chip-level heat before it reaches the surrounding airstream, allowing operators to preserve familiar server form factors while supporting rack densities well beyond 100 kilowatts. Vendors including Vertiv, Motivair by Schneider Electric, and CoolIT Systems have scaled manufacturing specifically to meet NVIDIA GB200 and GB300 rack requirements, reinforcing single-phase cold plate technology as the near-term standard for AI accelerator thermal management across US data center campuses.

Immersion cooling is projected to expand at the fastest pace through 2034 as US operators pursue higher rack densities without expanding chilled-water infrastructure. Submerging server components directly in dielectric fluid removes heat more uniformly than cold plates, eliminates hotspots across densely packed GPU boards, and cuts the water consumption associated with conventional chiller loops. Companies such as Green Revolution Cooling and Submer Technologies have expanded US manufacturing capacity in Texas to serve edge, colocation, and HPC customers seeking chiller-free deployment options, and growing interest from AI training clusters operating in water-constrained regions of the Southwest is expected to accelerate immersion cooling adoption over the forecast period.

Type categories include

• Single-Phase Direct-to-Chip Liquid Cooling (Dominating Segment)
• Immersion Cooling (Highest CAGR Segment)
• Two-Phase Direct-to-Chip Liquid Cooling
• Rear-Door Heat Exchangers
• Air-Assisted Liquid Cooling

Analysis by Component

Coolant distribution units held the largest revenue share within the US AI chip cooling market in 2025, as CDUs form the essential bridge between facility chilled-water loops and rack-level liquid cooling infrastructure. Operators deploying direct-to-chip and rear-door heat exchanger architectures require CDUs to regulate flow rate, pressure, and fluid temperature across GPU-dense racks that can draw well above 100 kilowatts. Manufacturers including Vertiv, nVent, and Motivair by Schneider Electric have introduced modular CDU families spanning 70 kilowatts to multi-megawatt row-based units, enabling both retrofit deployments in legacy facilities and greenfield builds engineered around gigawatt-scale AI campuses.

Liquid cold plates are forecast to register the fastest growth among AI chip cooling components as GPU thermal design power continues climbing toward and beyond 4,000 watts per accelerator. Precision-machined and brazed cold plate designs, including microchannel and jet-impingement variants, are being engineered in close coordination with chip vendors to remove concentrated heat flux directly from silicon packages. Manufacturers such as Eaton's Boyd Thermal business and JetCool, a Flex company, have expanded high-volume cold plate manufacturing in North America to keep pace with hyperscaler procurement cycles tied to successive NVIDIA and AMD accelerator generations.

Component categories include

• Coolant Distribution Units (Dominating Segment)
• Liquid Cold Plates (Highest CAGR Segment)
• Manifolds and Quick Disconnects
• Heat Exchangers
• Cooling Fluids and Chemistries

Analysis by Deployment

Hyperscale data centers accounted for the largest share of AI chip cooling deployment in the United States in 2025, driven by continued capital investment from major cloud service providers building dedicated AI training campuses across Texas, Virginia, and Georgia. These facilities house tens of thousands of GPUs per building, requiring standardized, centrally controlled liquid cooling architectures capable of scaling to multi-megawatt capacities. Vendors are increasingly co-developing reference designs directly with chip manufacturers to support hyperscale rollouts, as seen in nVent's collaboration with Siemens on an NVIDIA DSX Vera Rubin reference architecture, reinforcing hyperscale operators' central role in shaping US cooling technology adoption.

Colocation data centers are expected to post the fastest growth in AI chip cooling deployment as enterprises without in-house data center expertise turn to colocation providers to host GPU-dense AI workloads. Colocation operators are retrofitting existing air-cooled facilities with coolant distribution units, rear-door heat exchangers, and rack manifolds to accommodate tenant demand for high-density cabinets, often without a full facility redesign. This retrofit-oriented approach, supported by modular CDU offerings from vendors including Vertiv and Motivair by Schneider Electric, allows colocation providers to bring liquid-cooled capacity to market faster than new hyperscale construction, positioning the segment for above-average growth through 2034.

Deployment categories include

• Hyperscale Data Centers (Dominating Segment)
• Colocation Data Centers (Highest CAGR Segment)
• Enterprise Data Centers
• Edge Data Centers
• High-Performance Computing Facilities

Analysis by Application

AI training workloads represented the largest application segment for chip cooling demand in 2025, as large language model and generative AI development continues to require sustained, near-continuous GPU utilization across thousands of accelerators per training cluster. Training workloads generate consistently high and sustained thermal loads compared with more variable inference traffic, making liquid cooling technologies such as direct-to-chip and immersion systems essential rather than optional for facilities supporting frontier model development. Leading AI developers and cloud providers continue to concentrate training infrastructure investment in Texas and Virginia, reinforcing training workloads as the primary demand driver for advanced cooling technology adoption.

AI inference workloads are projected to grow fastest among cooling applications as enterprise adoption of AI-powered products shifts computing demand from a small number of large training runs toward continuous, distributed inference serving. Inference deployments are expanding across colocation and edge facilities closer to end users, requiring compact, rack-level liquid cooling solutions that can be deployed without extensive facility modification. As inference-optimized accelerators from NVIDIA, AMD, and custom silicon vendors proliferate across a broader base of data centers, demand for scalable direct-to-chip and hybrid air-liquid cooling systems suited to smaller, distributed deployments is expected to accelerate through the forecast period.

Application categories include

• AI Training Workloads (Dominating Segment)
• AI Inference Workloads (Highest CAGR Segment)
• High-Performance Computing
• Cloud and Virtualization Workloads
• Rendering and Visualization Workloads

Analysis by End User

Cloud service providers and hyperscalers represented the largest end-user segment in the US AI chip cooling market in 2025, reflecting their outsized share of capital expenditure on AI infrastructure. These operators directly influence cooling technology roadmaps through joint reference-design programs with chip manufacturers and cooling vendors, ensuring new GPU generations ship alongside pre-validated thermal management architectures. Hyperscaler procurement scale has also driven cooling vendors including Vertiv, nVent, and Motivair by Schneider Electric to expand US manufacturing capacity specifically to meet multi-gigawatt liquid cooling commitments tied to AI campus construction across the Southern and Western United States.

Colocation service providers are expected to be the fastest-growing end-user segment as enterprises and mid-sized AI developers increasingly lease GPU-dense capacity rather than building proprietary data centers. Colocation operators are partnering with cooling technology vendors to offer pre-integrated liquid-cooled cabinets as a standard leasing option, lowering the barrier for tenants to deploy high-density AI hardware without independent cooling infrastructure investment. This shift is particularly pronounced in secondary US markets outside traditional hyperscale hubs, where colocation providers are positioning liquid-cooling-ready capacity to capture overflow demand from capacity-constrained primary markets.

End User categories include

• Cloud Service Providers and Hyperscalers (Dominating Segment)
• Colocation Service Providers (Highest CAGR Segment)
• Enterprises
• Government and Research Institutions
• Telecommunication Companies

By Region

United States AI Chip Cooling Market Share 2025, by Region

North America

xx%

South America

xx%

Europe

xx%

Middle East Africa

xx%

Asia Pacific

xx%

The Southern United States held the largest share of the AI chip cooling market in 2025, supported by Northern Virginia's concentration of hyperscale and colocation facilities alongside rapidly expanding AI campuses across Texas and Georgia. Virginia's Loudoun County region remains the largest single data center market in the country, while Texas has attracted large-scale GPU training campuses supported by relatively accessible power interconnection and land availability. Cooling vendors including Vertiv, CoolIT Systems, and Green Revolution Cooling maintain manufacturing and service operations across the region to support this concentration of demand. State-level economic development incentives in Texas and Georgia, combined with proximity to grid capacity additions, continue to reinforce the South's position as the primary hub for liquid-cooled AI infrastructure deployment in the United States.

The Western United States is projected to register the fastest growth in AI chip cooling demand through 2034, driven by large-scale AI data center campuses under development across Arizona, Nevada, and Oregon. These states offer available land, competitive power pricing, and proximity to renewable energy generation, attracting new hyperscale AI campus announcements from major cloud providers. Water-constrained conditions across much of the Southwest are simultaneously accelerating adoption of closed-loop and immersion cooling technologies that minimize evaporative water consumption compared with traditional chiller-based systems. As new AI campuses in Arizona and Nevada move from construction to operational status over the forecast period, demand for direct-to-chip and immersion cooling equipment across the Western United States is expected to outpace the national average.

Regions Covered

Southern United States (Dominating Region)

• Virginia (Largest State Market)
• Texas (Fastest-Growing State Market)
• Georgia
• Florida

Western United States (Fastest-Growing Region)

• California
• Arizona
• Nevada
• Oregon

Midwestern United States

• Ohio
• Illinois
• Iowa

Northeastern United States

• New York
• New Jersey
• Pennsylvania

Market Share

The US AI chip cooling market is consolidated and undergoing rapid structural change as power management, HVAC, and industrial chemistry companies acquire specialist liquid cooling firms to build integrated grid-to-chip portfolios. Vertiv, Eaton's Boyd Thermal business, Schneider Electric's Motivair, and nVent hold leading positions through established hyperscaler relationships and large-scale US manufacturing capacity, while Ecolab, Trane Technologies, and Daikin have entered the market through the 2025-2026 acquisitions of CoolIT Systems, LiquidStack, and Chilldyne respectively. Independent specialists such as ZutaCore, Accelsius, Green Revolution Cooling, and Submer continue to compete on emerging two-phase and immersion technologies. Key success factors include co-engineering relationships with GPU manufacturers, US manufacturing scale to meet hyperscaler procurement timelines, and proven reliability at multi-megawatt rack densities.

Key Players

• Vertiv Holdings Co. (United States)
• Eaton Corporation plc (Ireland)
• Schneider Electric SE (France)
• nVent Electric plc (Ireland)
• Ecolab Inc. (United States)
• Trane Technologies plc (Ireland)
• Green Revolution Cooling, Inc. (United States)
• ZutaCore, Inc. (United States)
• Accelsius LLC (United States)
• Flex Ltd. (Singapore)
• Delta Electronics, Inc. (Taiwan)
• Daikin Industries Ltd. (Japan)
• Munters Group AB (Sweden)
• Submer Technologies S.L. (Spain)
• Iceotope Technologies Ltd. (United Kingdom)

Recent Market Developments

• In November 2025, Daikin Applied acquired Chilldyne, a US-based provider of negative-pressure direct-to-chip liquid cooling systems, extending Daikin's data center cooling portfolio to include chip-level liquid cooling technology alongside its existing HVAC offerings for AI and high-performance computing facilities.
• In November 2025, Eaton signed a definitive agreement to acquire the Boyd Thermal business of Boyd Corporation for USD 9.5 billion, adding Boyd's liquid cold plate and coolant distribution unit manufacturing capabilities to Eaton's power management portfolio; the transaction closed in the first quarter of 2026.
• In January 2026, Motivair by Schneider Electric introduced the MCDU-70, a 2.5-megawatt coolant distribution unit scalable to 10 megawatts and beyond, designed to support gigawatt-scale AI data center campuses built around NVIDIA's Omniverse DSX Blueprint reference architecture.
• In March 2026, Trane Technologies announced a definitive agreement to acquire LiquidStack, a Texas-headquartered liquid and immersion cooling technology provider, expanding Trane's end-to-end thermal management portfolio for hyperscale and AI-scale data center customers; the transaction closed in March 2026. 

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