September 4, 2026 — The global AI Power Supply Units Market is projected to reach USD 6,298.1 million by 2036, rising from USD 698.3 million in 2026 at a 24.6% CAGR, according to Fact.MR. The market was valued at USD 560.4 million in 2025 and is expected to create an absolute dollar opportunity of USD 5,599.8 million between 2026 and 2036. [LINK: AI Power Supply Units Market]

The expansion is closely tied to rising power requirements across AI servers, GPU clusters, hyperscale cloud infrastructure, and high-performance computing environments. Fact.MR identifies growing demand for high-efficiency, high-wattage power systems as a key factor behind the market's rapid expansion.

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AI Power Supply Units Market Gains From Rising AI Power Density

AI workloads require greater power capacity and more effective thermal management. Fact.MR states that increasing deployment of artificial intelligence, machine learning, and high-performance computing workloads is raising power-density requirements in modern data centers.

Hyperscale and colocation data centers are also increasing demand for high-capacity and high-efficiency power supply units. Energy efficiency and total cost of ownership are influencing purchasing decisions, while modular and hot-swappable designs support reliability in mission-critical facilities.

Liquid cooling is becoming another area of interest. Fact.MR reports that liquid-cooled PSUs accounted for 42% of the market in 2026, compared with 58% for air-cooled PSUs. The report links liquid cooling with higher-density computing and improved heat transfer in demanding AI environments.

Shambhu Nath Jha, Principal Consultant at Fact.MR, said:

“Growth is increasingly driven by hyperscale AI data center expansion, rising demand for high-density GPU clusters, and the shift toward ultra-efficient, high-wattage, and liquid-cooled PSU architectures.”

The report also identifies rapid AI data center expansion, rising energy consumption, and the move toward higher-wattage and redundant architectures as major growth factors. [LINK: U.S. Department of Energy AI Data Center Efficiency Program]

Multi-Rail PSUs Hold 62% Share in 2026

Multi-rail PSUs are expected to lead the product type segment with a 62% share in 2026. Fact.MR attributes this position to improved load distribution, safety, and reliability in dense AI server environments.

Single-rail PSUs account for the remaining 38% share and are widely used in simpler and cost-sensitive server configurations.

The 1,000W–3,000W output power segment holds the largest share at 46% in 2026. Fact.MR links this position to demand from mainstream AI servers, enterprise deployments, hyperscale facilities, and GPU clusters.

OEM/Embedded PSUs dominate deployment with a 78% share in 2026. Their integration with AI servers and direct procurement by hyperscale operators support their leading position.

“Multi-rail, high-wattage, and OEM-integrated PSUs lead due to performance and scalability advantages.”

This second quote is drawn from the report's published analyst commentary and should receive editorial sign-off because the source provides only one named analyst.

India Leads Country Growth Forecast

India is projected to record the fastest country-level growth, with demand for AI Power Supply Units expected to rise at a 28.9% CAGR through 2036. China follows at 27.6%, while South Korea is projected to grow at 26.4%.

The United States is expected to record a 25.8% CAGR, Japan 24.6%, Canada 23.5%, and Germany 21.2% over the forecast period.

Fact.MR links India's growth to digital transformation, government policy support, cloud adoption, edge deployments, and large-scale AI data center investments. The report notes that the Government of India expanded data center policy incentives in June 2025, while Reliance Jio announced large-scale AI data center projects in October 2025.

In China, Fact.MR points to the expansion of the “Eastern Data, Western Computing” program in July 2025 as a development supporting data center construction and AI infrastructure demand.

Competitive Landscape

The AI PSU market is described by Fact.MR as semi-consolidated. Delta Electronics, LITE-ON Technology, AcBel Polytech, and FSP Group collectively hold around 60%–70% share, particularly in hyperscale deployments.

Other companies identified in the report include Advanced Energy, OmniOn Power, Shenzhen Megmeet Electrical, Great Wall Power Technology, Aohai Technology, and Shenzhen Oulutong Electronics.

Competition centers on efficiency, power density, reliability, OEM relationships, scale, and supply-chain control. Fact.MR states that multi-supplier strategies and long-term contracts used by hyperscale buyers can limit pricing power and increase the importance of technological differentiation.

Delta Electronics has also made several recent moves. In April 2026, Delta Electronics introduced an integrated “grid-to-chip” infrastructure combining power, cooling, and control systems for high-density AI workloads. In April 2026, the company also showcased AI-driven power solutions at Hannover Messe.

Market Definition and Research Scope

AI Power Supply Units are specialized power devices designed to provide stable, high-efficiency energy to AI servers and data center equipment. Fact.MR defines the market around PSU systems serving GPUs, CPUs, and accelerators used in artificial intelligence workloads.

The study covers product type, output power rating, form factor, cooling mechanism, deployment type, application, end-use industry, and region. Its research combines interviews with PSU manufacturers, data center operators, hyperscale cloud providers, component suppliers, and industry experts with desk research.

Fact.MR estimates market size using a hybrid approach that combines AI server shipments, PSU units per server, average selling prices, data center power spending, and AI infrastructure investments.

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About Fact.MR

Fact.MR is a market research and consulting company providing industry intelligence, market forecasting, competitive analysis, and strategic research across global markets. Its research combines primary interviews, secondary sources, market sizing, forecasting, and ongoing data validation.