How to Reduce AI Power Consumption in the Data Center

AI unquestionably comes with a hefty carbon footprint. While most organizations have made pledges around sustainability and minimizing e-waste, data center power demand could triple by 2028, and it’s data centers that provide the cloud computing that powers AI. 

That’s why we’ve started talking about “energy intelligence”: the ability to measure, predict, and optimize how energy is consumed across digital infrastructure. Energy intelligence turns power from a cost center into a controllable, strategic resource.

Per our recent joint report with MIT Technology Review: 100% of execs expect energy management to be a core metric soon. That means energy, and how you save it and use it, is now mission-critical.

Data storage has taken center stage in energy intelligence, with AI being the leading driver of storage growth and AI and ML workloads consuming an average of 24% of storage infrastructure. 

Being “AI-ready” is more than just about making sure your company can take full advantage of AI to keep up with competitors. It’s also about making sure you can do this without driving up operational costs, contributing to environmental damage, and potentially incurring massive regulatory fines.  

Reducing power consumption dampens the environmental impact of data centers and keeps operational costs down, enabling businesses to expand AI adoption without running into capacity limits or prohibitive energy costs. 

As companies look to save money and improve their environmental sustainability scores, a reduction in AI power consumption presents a clear win-win opportunity. 

Understanding AI-based Power Consumption: The ‘FinOps’ Parallel

A decade ago, cloud adoption created a new operational challenge: costs scaled faster than visibility. FinOps emerged to bring discipline to cloud spending—tracking usage, allocating costs, and optimizing in real time.

AI is creating a similar inflection point, but the constraint isn’t just financial—it’s physical. Energy consumption is rising faster than most organizations can measure or manage it.

In that sense, AI has a FinOps problem—just not where most teams expect.

The computational intensity of AI workloads, particularly for training ML models and deep learning models, requires major processing power, which leads to high electricity usage. These models often involve complex algorithms and very large data sets, requiring specialized, high-performance hardware. 

The constant need for data retrieval, processing, and storage in real-time AI applications means even more energy consumption. There’s also the infrastructure supporting these operations, like cooling systems and backup power supplies. 

To evaluate overall energy efficiency, data center managers use a metric known as power usage effectiveness (PUE): the ratio of total energy consumption at the facility to the energy consumed by IT equipment alone. A lower PUE indicates greater efficiency, meaning a larger proportion of energy is used directly for computing rather than supporting infrastructure. In the context of AI operations, optimizing PUE is essential because the high energy demands of AI workloads can exacerbate inefficiencies. 

Let’s look at the five best ways to reduce AI-related power consumption in the data center. 

1. Optimize Your Algorithms

Data centers use most of their energy to operate processors and chips. Like other computer systems, AI systems process information via zeros and ones. Every time a bit changes between one and zero, it consumes electricity and generates heat. Roughly 40% of a data center’s electricity usage goes toward air conditioners to keep servers cool so they continue functioning. 

Optimizing AI algorithms means creating more efficient AI training models, which means less parameters for them to process, which means less changes from zero to one, which means less energy consumption, which means less heat, which means less energy required to keep servers cool. 

It all adds up to better use of AI data and less money spent. 

Techniques for optimizing AI algorithms include:

AI Data Pruning

Pruning involves removing less important neurons or connections in a neural network to reduce its size and computational load without significantly impacting performance. This can be done through techniques like weight pruning (removing weights with small magnitudes) or neuron pruning (removing entire neurons). As an example, in AlexNet, pruning 90% of the parameters reduced the model size by 9x with only a small drop in accuracy. Another example of AI data pruning is the use of small language models (SLMs), which are easier to train, provide higher accuracy, and use less power per computation than large language models. 

Quantization

Quantization reduces the precision of the numbers used to represent the model’s parameters. Instead of using 32-bit floating-point numbers, 16-bit, 8-bit, or even lower precision can be used.

Post-training quantization and quantization-aware training are common methods. The former applies quantization after training, while the latter integrates it during the training process. An example would be TensorFlow Lite’s quantization, which can reduce a model’s size by up to 4X and improve inference speed by up to 3X with minimal impact on accuracy.

Compression

Compression reduces the storage space required for a model by using methods, such as parameter sharing, low-rank factorization, or encoding techniques. Methods like Huffman coding, weight sharing, or singular value decomposition (SVD) can be employed to compress neural networks. The Deep Compression framework compressed deep neural networks by 35X to 49X without loss of accuracy through pruning, quantization, and Huffman coding.

2. Rethink Your AI-supporting Hardware 

AI is only as efficient as the hardware that supports it. Having the most efficient hardware possible becomes increasingly important as AI models grow in complexity since hardware choice can significantly influence power efficiency, speed, and overall cost of AI operations. 

AI model computation often requires the support of high-performance CPUs, GPUs, and specialized AI accelerators (e.g., TPUs), but these powerful processors tend to consume massive amounts of energy and create bottlenecks that ultimately affect a model’s performance. The amount and type of memory (RAM, VRAM) influence energy consumption. More memory and faster access speeds typically require more power, but high-power components generate more heat, necessitating efficient cooling solutions. 

SSDs are generally more energy efficient than HDDs, and ultimately less costly. All-flash arrays are changing the data storage game by maximizing speed, performance, and flexibility. 

For CPUs:

• AMD EPYC and Intel Xeon are server-grade processors that offer high performance with power efficiency, making them suitable for AI workloads in data centers.
• ARM-based processors are known for their energy efficiency and are becoming popular for AI inference tasks, especially in edge and mobile applications.

For GPUs:

• NVIDIA A100 is designed for AI training and inference, offering very high performance per watt thanks to its Ampere architecture.
• AMD Instinct MI100 is optimized for AI and machine learning workloads with a focus on energy efficiency.
• NVIDIA’s Jetson series provides powerful AI capabilities with low power consumption.

For AI accelerators, consider:

• Google TPUs, which are custom-developed for AI workloads 
• Edge TPUs, which are designed for on-device AI processing 

For data storage, NVMe (non-volatile memory express) SSDs provide fast data access with lower power consumption compared to traditional HDDs.

3. Rewild Your Data Center’s Efficiency

Changing the way your data center works by changing its design and infrastructural components can be a major project, but it’s one you can tackle in phases to ultimately arrive at a much more efficient data center that uses far less energy than it did before, meaning money saved and AI processing power increased. 

Here are the most powerful ways to make your data centers use less power:

Efficient Cooling Solutions

Cooling represents a significant portion of a data center’s energy usage, often accounting for nearly half of overall power consumption. Investing in advanced cooling technology like liquid cooling can enhance overall energy efficiency by reducing the need for power-intensive air conditioning systems. Another option is hot aisle/cold aisle containment, which involves organizing server racks into rows with alternating hot and cold aisles. Cold air is directed to the front of the servers through the cold aisles, while hot air is expelled through the hot aisles and contained, preventing it from mixing with the cold air. This configuration enhances cooling efficiency by maintaining a consistent airflow and temperature, reducing the workload on cooling systems. These types of cooling strategies can lead to big energy savings and reduce your data center’s reliance on power-hungry air conditioning units.

Power Management Techniques

Power management techniques like dynamic voltage and frequency scaling (DVFS) can also improve the energy efficiency of data centers. DVFS enables processors to adjust their voltage and frequency according to workload demands. This requires hardware that supports DVFS, and administrators must configure operating systems to enable it. By using DVFS intelligently, you can reduce power consumption during low-demand periods without sacrificing performance during periods of peak activity.

Modularization

Modular data centers built using prefabricated units allow for incremental expansion and reconfiguration, enabling data centers to scale their operations without overprovisioning resources. Modular designs also facilitate better cooling and power distribution, as each module can be optimized for specific energy-efficient configurations. 

Renewable Energy

Data centers are increasingly using renewable energy to reduce costs, improve efficiency, and meet government-led energy initiatives, such as the U.S. Department of Energy’s net-zero 2050 project. Many enterprises are prioritizing using sustainable energy practices in their data centers. Solar, wind, and other renewables support corporate sustainability goals while providing reliable and cost-effective energy usage. 

4. Use Cloud Computing and Virtualization

Cloud computing can significantly reduce the AI energy footprint through several mechanisms:

• Resource sharing: Cloud providers use multi-tenant environments where multiple customers share the same physical resources. This leads to better use of servers and reduces the need for excess capacity, thereby saving energy.
• Efficient data centers: Cloud providers invest heavily in optimizing their data centers for energy efficiency. They often use advanced cooling techniques, efficient hardware, and renewable energy sources, which can be more efficient than on-premises data centers.
• Scalability: Cloud platforms allow for dynamic scaling, meaning that resources can be allocated based on demand. This prevents overprovisioning and underutilization, which are common in on-premises setups.
• Geographic optimization: Cloud providers have data centers around the world. Workloads can be moved to locations where energy is cheaper and greener, further reducing the carbon footprint.

Virtualization also plays a crucial role in reducing energy consumption via:

• Server consolidation: Virtualization allows multiple virtual machines (VMs) to run on a single physical server. This reduces the number of physical servers needed, leading to lower energy consumption.
• Dynamic resource allocation: Virtual machines can be migrated between physical servers to balance loads and ensure efficient resource use. This can minimize the number of active servers, thus saving energy.
• Idle resource reduction: Virtualization technologies can power down idle resources or consolidate workloads during off-peak times, reducing unnecessary energy use.

5. Get “Smart” by Monitoring, Analyzing, and Acting

“Smart” data centers are now a thing. Real-time monitoring and analytics help data center managers identify power consumption patterns, pinpoint inefficiencies, and understand peak usage times. This allows for more informed decisions on optimizing energy use, such as adjusting cooling systems, consolidating workloads, and implementing power-saving features. Real-time data helps in proactively managing power consumption, ensuring that resources are used efficiently and that costs are kept under control.

A key part of minimizing your data center’s AI-based environmental footprint is knowing how your energy is being used so you can maximize efficiency. 

The best way to do this is to use all the tools and techniques at your disposal, including:

• Cloud provider tools: Major cloud providers like AWS, Google Cloud, and Microsoft Azure offer built-in monitoring tools (e.g., AWS CloudWatch, Google Cloud Monitoring, Azure Monitor) that track resource usage and energy consumption of cloud-based workloads.
• Specialized energy monitoring software: Tools like Intel Power Gadget, NVIDIA’s nvidia-smi, and AMD’s ROCm provide detailed insights into the energy usage of specific hardware components.
• Data center management software: Tools like VMware vRealize Operations, Schneider Electric EcoStruxure, and Cisco Data Center Analytics help monitor and manage the energy consumption of on-premises data centers.

Energy consumption techniques include:

• Energy profiling: Measure and analyze the energy consumption of different components (CPU, GPU, memory, storage) to identify the most energy-intensive parts of the AI workload.
• Power capping: Implement power capping techniques to limit the maximum power consumption of hardware, ensuring that it operates within a specified energy budget.
• Dynamic voltage and frequency scaling (DVFS): Adjust the voltage and frequency of processors based on the workload demand to optimize energy usage.
• Off-peak scheduling: Schedule energy-intensive AI tasks during off-peak hours when energy costs and demand are lower. This can also help balance the load on the power grid.
• Batch processing: Aggregate smaller tasks into larger batches to optimize resource utilization and reduce the energy overhead associated with frequent task switching.
• Dynamic load balancing: Use dynamic load balancing techniques to distribute workloads evenly across available resources. This prevents overloading individual servers and ensures more efficient energy use.

Read the full story on how Google is partnering with Indiana Michigan Power and the Tennessee Valley Authority to pause some of its non-essential AI workloads when the power grid is under stress. 

How Everpure Helps You Reduce AI-based Power Consumption

We’re at an AI and sustainability inflection point. While most companies don’t yet fully understand AI’s impact, Everpure does. That’s why we’ve prioritized ESG and sustainability as part of our core mission. Pure1^®, for example, uses predictive analytics to help data center managers better understand performance and capacity needs, optimize energy efficiency, and secure their critical data. Pure1 provides comprehensive monitoring and predictive maintenance for storage arrays, leveraging AI to analyze performance data and predict potential issues. 

By prioritizing the monitoring and management of AI-related energy consumption, organizations can achieve a balance between performance, cost efficiency, and environmental sustainability. These practices not only contribute to a greener planet but also enhance the overall efficiency and reliability of AI operations.

Learn more about how Everpure helps you fully capitalize on the AI opportunity without having to compromise.

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FAQ

Why does AI consume so much power in the data center?

AI workloads require intense compute and data movement, which drives high power usage across CPUs, GPUs, memory, and storage systems. The combination of large models and frequent training or inference cycles amplifies energy demand.

How can storage infrastructure impact overall AI power consumption?

Storage systems that are inefficient, bottlenecked, or siloed can increase power use by forcing repeated data transfers and prolonging compute activity. Optimized storage can reduce idle energy and speed data delivery, lowering total consumption.

What practices help reduce energy use for AI workflows?

Strategies include consolidating data storage, improving data locality, choosing energy-efficient hardware, optimizing cooling, and implementing software that schedules workloads during lower-impact power periods.

Does data reduction affect power usage?

Yes. Techniques such as deduplication and compression reduce the amount of physical data that needs to be stored or moved. Less data movement and smaller storage footprints can lead to lower power draw.

Can modern infrastructure help control cooling costs?

Efficient hardware and consolidated system designs can reduce heat output, which in turn lowers cooling requirements. Smart infrastructure also enables better thermal management across rack and facility levels.

What role does software optimization play in reducing power consumption?

Software that orchestrates workloads efficiently, minimizes unnecessary data movement, and balances compute loads can significantly cut power usage. Intelligent scheduling ensures hardware is used when most effective and idle time is minimized.

Is it better to centralize or decentralize AI processing to save energy?

The best approach depends on workload patterns and data locality. Centralizing frequently accessed data near compute can reduce repeated transfers, while decentralized processing may be efficient for edge or latency-sensitive tasks.