Artificial Intelligence

We power AI - from grid to core

We power AI by enabling todays and future generation of energy-efficient (AI) data centers with best-in-class performance, reliability, and lowest total cost of ownership. With our comprehensive semiconductor solutions and system expertise, we support customers in optimizing their data centers - from grid to the core.

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Powering AI
Article

Sep 01, 2026

Paving the way for efficient, reliable, and trustworthy AI

Artificial Intelligence is accelerating demand for computing power at an unprecedented scale. To unlock its full potential, AI infrastructure must combine performance, efficiency, reliability, and sustainability across the entire power chain. From grid connection to processor core, innovative power technologies are becoming a key enabler of the next generation of AI.

Meeting that challenge sustainably requires more than incremental improvements. It demands a fundamental rethink of how power is generated, transmitted, stored, and delivered — backed by the latest advancements in semiconductor technology.

Key figures on the energy demand of AI

1 question to ChatGPT needs 10x the energy of 1 Google search
1 question to ChatGPT needs 10x the energy of 1 Google search 1 question to ChatGPT needs 10x the energy of 1 Google search 1 question to ChatGPT needs 10x the energy of 1 Google search
Training an AI model needs the energy output of 25 wind turbines Training an AI model needs the energy output of 25 wind turbines Training an AI model needs the energy output of 25 wind turbines
The power demand of a single AI data center could power 3 million homes The power demand of a single AI data center could power 3 million homes The power demand of a single AI data center could power 3 million homes
AI data centers expected to need 90% more electricity by 2027 compared to 2022 AI data centers expected to need 90% more electricity by 2027 compared to 2022 AI data centers expected to need 90% more electricity by 2027 compared to 2022
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Three levels of power challenge

The training of ever larger AI models in huge data centers requires ever more powerful computing capabilities and a clustering of as many as 100.000 processors into one virtual machine. This will pose challenges at three levels: 

  • Powering modern processors at ever higher load currents and strong transient load steps. We are expecting up to 10.000 Ampere per processor within this decade, a tenfold increase from the requirements seen today.
  • Powering AI server racks at power levels beyond 1 MW. This is again a tenfold increase in rack power from the current state-of-art.
  • Powering entire data centers at power levels in the GW scale, requiring a different infrastructure and novel ways of power distribution across the data center. Furthermore, as data centers are turning into substantial consumers of electricity, buffering of load profiles and provision of ancillary grid services become a necessity.

How will the future of power management look like?

In our whitepaper "The Future of Powering AI" we share our insights on some likely scenarios in the future of AI power management. We examine how changes in architecture, quality, efficiency, thermal requirements, and energy availability will shape the landscape. Our analysis aims to provide a clear understanding of the critical trends in this evolving field.

Download whitepaper

What challenges do AI data centers face and how do semiconductors help solve them?

AI is driving unprecedented demand for computing power. As processors, server racks, and data centers continue to scale, AI infrastructure must become more efficient, reliable, and sustainable.

How can AI processors get the power they need in less space?

1,650 GW waiting to connect

Challenge
AI processors require extremely high current and fast transient response. Traditional lateral power delivery consumes too much board space and increases power delivery losses at very high current levels.

Solution
Vertical power delivery brings power through the board toward the processor backside. Infineon supports this transition with low-voltage silicon MOSFETs, discrete power stages and vertical power modules designed for high current density and efficiency.

Silicon carbide CoolSiC™ MOSFETs
High density power modules

How can AI racks scale beyond today’s power limits?

Challenge
Power levels in AI racks are rising rapidly as more GPUs and accelerators are clustered inside dense systems. Established 48 V architectures and single-phase power supplies become harder to scale at these levels.

Solution
Higher-voltage DC architectures, disaggregated power shelves, high-efficiency PSUs, BBUs and protection functions can help AI racks scale while reducing conversion losses and improving power density.

Powering AI PSU solutions
Powering AI BBU solutions

How can entire AI data centers draw power more efficiently and reliably?

1,650 GW waiting to connect

Challenge
AI data centers increasingly become large electricity consumers. Fast-changing GPU workloads can stress power infrastructure, while operators need high uptime, grid stability and better energy efficiency.

Solution
Infineon’s grid to core portfolio supports efficient conversion, protection, monitoring and buffering across the power chain, including solid-state transformers, solid-state circuit breakers, BESS, PSUs, IBCs and power reliability modeling.

Easy power modules for SSTs
Next-generation solid-state power distribution

How can AI growth align with environmental responsibility?

1,650 GW waiting to connect

Challenge
AI workloads require enormous computing power, cooling and supporting infrastructure. Without efficiency improvements, scaling AI can increase energy consumption and emissions.

Solution
Advanced power semiconductors can reduce losses at multiple conversion stages. Combined with renewable energy, efficient architectures and real-time power health monitoring, they help make AI infrastructure more sustainable and economically viable.

Supporting AI power demands sustainably
Empowering green energy with WBG semiconductors

How does power flow from grid to core?

AI data centers require efficient power delivery at every stage of the energy chain. Electricity generated from renewable sources is stabilized through battery energy storage systems (BESS), converted and managed by solid-state transformers (SSTs), and protected by solid-state circuit breakers (SSCBs) before reaching the data center. Inside the facility, advanced power conversion and distribution solutions deliver reliable power to server racks and ultimately the AI processors.

How does power flow from grid to core

Beyond the buzz: The carbon footprint of AI

Behind the brilliance of AI lies a computationally and power-intensive process - with a staggering carbon footprint. As we expand AI capabilities, we need to be aware of the massive energy consumption of AI data centers.

100,000 KM global HVDC lines

Training one large AI language model produces as much CO₂ as 5 cars over their entire lifetime.*

Worldwide, energy savings of around 48 TWh could be achieved with various types of our advanced power semiconductors. This corresponds to more than 22 million tons of CO₂ emissions, according to Infineon analysis.

*Source: CNBC

Powering a sustainable future: Meeting the challenge of data center power demand

Looking into the future there are many technological challenges ahead we need to address all while continuously enhancing energy efficiency and performance. We need to foster bringing clean and reliable energy to the AI data centers. It is about enabling the sustainable growth of AI technologies in a way that is compatible with our environmental responsibilities. After all, there’s no AI without power. This reality drives us to keep advancing our technologies, ensuring that as AI evolves, our solutions for powering it efficiently and effectively evolve as well.

FAQ

Why does AI need so much power?

AI models rely on large amounts of data and complex calculations. Training and running advanced models require many processors, high-speed networking, cooling and supporting power infrastructure. As workloads grow, the power chain from grid to processor core must become more efficient and scalable.

What does “from grid to core” mean?

“From grid to core” describes the complete power path into an AI data center: grid connection, conversion, protection, backup, rack-level distribution, intermediate conversion and final voltage regulation close to the AI processor core. Improving each step helps reduce losses and improve reliability.

Which Infineon technologies support AI data center power?

Relevant technologies include power semiconductors based on silicon, silicon carbide and gallium nitride, plus power stages, vertical power modules, PSUs, BBUs, IBCs, hot-swap and protection solutions, solid-state transformers, solid-state circuit breakers and power reliability modeling.

How can semiconductors improve AI data center efficiency?

Every AC/DC and DC/DC conversion step can introduce losses. Advanced power semiconductors can reduce conduction and switching losses, enable higher switching frequencies, improve thermal behavior and increase power density. This can help lower energy use, cooling effort and system size.

How can AI data centers become more sustainable?

Sustainability depends on efficient power conversion, high system reliability, better power monitoring, integration of renewable energy and architectures that minimize unnecessary conversion steps. Infineon positions power semiconductors as key enablers for scaling AI while supporting environmental responsibility.

 

Enjoy ultimate design flexibility

With the industry's broadest power semiconductor portfolio and deep expertise in silicon, silicon carbide, and gallium nitride technologies, we enable efficient, reliable, and scalable AI infrastructure. Our solutions support every stage of the power chain, from grid to core.