Managing energy consumption is one of the biggest challenges to turn a nation’s AI vision into reality. AI data centers require vast power resources at a time when the national grids are shifting toward renewables. Another major hurdle is talent. With global competition for AI expertise heating up, countries must invest more in education and training. There should also be more industry collaboration to build the skilled workforce needed for a true independent AI vision.
Senior Manager CSP Product Marketing at Broadcom.
AI workloads and energy use
AI workloads, particularly those associated with large language models (LLMs) and advanced analytics, impose varying energy demands. Training AI models is an extremely computationally intensive process, requiring stable, high-energy inputs over extended periods. It involves feeding large datasets into deep learning models, running complex calculations, and iterating repeatedly to refine accuracy.
This process demands high-performance computing resources and an uninterrupted power supply, making it one of the most energy-consuming aspects of AI.
In contrast, AI inference runs models in real-time to make predictions, classify data, or analyze text, images, and video. Though less demanding than training, inference workloads are dynamic and need efficient and steady energy resource allocation for real-time tasks like chatbots, automation, and edge computing.
So how can we manage the energy consumption from these intensive AI workloads?
Renewable energy: A double-edged sword
Renewable energy is central to the UK’s AI Action Plan and its ambitions to become a leader in AI data centers. With substantial resources in wind, solar and hydro contributing 36.1% of electricity generation in 2023, the UK can tackle the growing electricity demand in a more environmentally sustainable manner.
The UK’s newly established AI Energy Council is expected to explore innovative energy solutions, such as Small Modular Reactors (SMRs), to bridge this gap. With AI-driven energy consumption accelerating, a 160% increase in data center power demand is anticipated.
Despite hardware efficiency gains seen in AI adoption and real-world scenarios, increasing demand for the technology outpaces these improvements. Popular AI-driven solutions, such as ChatGPT, have seen rapid user adoption, surpassing 100 million users in 2025 with approximately 464 million visits per month.
The International Energy Agency reports that a single ChatGPT query requires 2.9 watt-hours of electricity, nearly ten times more than a Google search, which only needs 0.3 watt-hours.
As AI continues to scale, the growing energy consumption raises important concerns about environmental sustainability, highlighting the need for strategic solutions.
Aligning AI workloads with renewable energy and advanced resource management
It is clear that renewable energy alone is insufficient in meeting the UK’s AI Action Plan’s requirements, which presents a chance for AI data centers to adopt intelligent workload scheduling and resource management strategies. AI workloads should be scheduled to coincide with periods of peak renewable energy generation, such as high-wind periods or midday solar peaks.
This approach allows AI training tasks, which require significant power, to be executed when renewable energy availability is at its highest, reducing reliance on non-renewable backup sources or storage technologies such as batteries.
AI requires high levels of compute resources, typically utilizing specialized hardware like GPUs, which handle high levels of parallel transactions essential for AI models and applications. Multi-tenanted GPU virtualization and graphics virtualization solutions effectively consolidate resource utilization, reducing the need for additional hardware and energy consumption.
GPUs are significantly more energy-efficient than CPUs for AI inference tasks—studies show up to 42x greater efficiency—but their increasing cost and energy intensity make strategic allocation crucial. Given the complexity of GPU scenarios, which vary depending on applications, query types, and user volume, ensuring these powerful resources are fully utilized and not left idle is a top priority for reducing environmental impact and maximizing return on investment.
Effective GPU optimization strategies include dynamic sharing and partitioning techniques, enabling better resource allocation, minimizing wastage, and supporting data centers transitioning to renewable energy sources.
AI schedulers should be designed to scale compute resources up or down based on real-time energy availability. This means distributing (within data proximity requirements) workloads across different geographic locations where renewable energy is abundant at any given time and adjusting processing speeds to match fluctuating renewable energy supplies.
Further boosting energy efficiency in data centers requires innovative solutions, like liquid cooling and AI-driven optimization, with advanced designs and hardware that minimize energy consumption. A diversified energy mix is also key, combining renewables with technologies like SMRs to ensure a stable power supply, supported by data center energy monitoring and allocation modelling.
Government agencies can also drive environmental sustainability by financially incentivizing data centers to run on renewable energy while managing growth to protect the energy grid. These strategies ensure consistent power availability while maximizing the use of renewable energy when conditions are favorable.
Building a future of innovation and environmental sustainability
The UK is well-placed to achieve its AI ambitions without overwhelming the energy grid, provided it embraces a portfolio of efficiency levers across workload, hardware and infrastructure layers. Physical virtualization is one of the most immediate and proven techniques. Deployments of advanced virtualization platforms can cut physical servers by 39 % and trim three-year infrastructure cost by 34 %, according to IDC’s 2024 study.
Fewer racks translate directly into a lower baseload on the grid and quicker alignment with renewable-energy contracts. AI acceleration now benefits as well; tests have shown that virtualization solutions with GPU support delivers AI training performance within 1–6 % of bare metal and inference at 94–105 % yet still leave up to 88 % of CPU cores free for other work. Multi-tenant GPU virtualization therefore drives higher AI throughput per watt, deferring additional hardware purchases and the embodied carbon they carry.
Alongside virtualization, emerging technologies such as liquid cooling, AI-driven energy-optimization software and diversified power sources (including small modular reactors) will further curb data-center consumption. While no single solution is a silver bullet, the strategic combination of consolidated, software-defined infrastructure and intelligent energy management positions the UK to set a global example—demonstrating how cutting-edge AI capability and energy security can advance together on a clear trajectory to net-zero.
By prioritizing environmentally sustainable and sovereign approaches, the UK has a unique opportunity to set a global example – demonstrating how cutting-edge AI and energy security can evolve together.
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