AI Is an Energy Glutton. That Needs to Stop.

AI needs to stop consuming so much energy to continue at its current pace.

Written by JB Baker
Published on Jul. 08, 2024
AI Is an Energy Glutton. That Needs to Stop.
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Artificial intelligence has made headlines for its incredible advancements. Its voracious appetite for energy is also capturing attention. 

5 Ways to Cut AI Energy Consumption

  1. Optimize AI models using techniques like model pruning.
  2. Use more energy-efficient hardware such as arm-based processors.
  3. Manage workloads so AI tasks are done when other demands for energy are low.
  4. Practice distributed computing.
  5. Lessen the need for data centers by integrating processing capabilities directly into storage.

Simply powering the GPUs that NVIDIA expects to ship in 2027 will consume more power than many small countries do in a year, according to Scientific American. A single ChatGPT query consumes nearly 10 times the power required for a Google search, according to a Goldman Sachs report.

But does it have to be this way? Can we put AI on a sustainable diet without sacrificing its performance? Let’s dive into the growing energy demands of AI, the implications for global sustainability and how innovative technologies can help trim AI energy consumption.

Related Reading The Future of AI Is Nuclear

 

Why Is AI So Hungry?

Several factors create AI’s insatiable appetite for energy, including data volume, complex algorithms, the fact that AI runs 24/7 and high-performance hardware.

AI models, particularly deep learning algorithms, require vast amounts of data to train effectively. The larger the dataset, the more computational power is needed to process it. AI models are also becoming more complex, with deeper neural networks and more parameters that demand increased computational resources.

Many AI applications run 24/7, processing data in real time and making predictions. This constant operation results in significant energy consumption. Finally, AI training and inference often rely on powerful GPUs and specialized hardware like tensor processing units (TPUs).  While these specialized processors have higher processing capabilities per core and per chip than the x86-based processors, they are also more power-dense. For example, NVIDIA’s Blackwell GPU consumes up to 1000W. 

The energy demands of AI present a serious challenge to global energy sustainability efforts. Data centers, which house the hardware for AI computations, already consume a substantial portion of the world’s electricity. Data centers accounted for about 2 percent of global electricity use in 2022 and could more than double by 2026, according to the International Energy Agency. This growing demand is straining the energy supply grid, with utilities scrambling to keep up.

The carbon footprint of these energy-intensive operations is substantial, contributing to climate change and environmental degradation. As AI continues to grow, so does its impact on our planet. Strategies to reduce consumption and make energy use more efficient can lessen this impact.
 

How to Reduce AI’s Energy Consumption

Several strategies can curb AI’s energy appetite without stunting its growth. 

Optimizing AI Models

Simplifying and optimizing AI models can significantly reduce their energy consumption. Techniques such as model pruning, quantization and distillation can help streamline models, making them more efficient without sacrificing accuracy. By reducing the number of parameters and operations required, these methods decrease the computational load and energy usage.

Energy-Efficient Hardware

Switching to energy-efficient hardware and specialty processors is another effective strategy. Arm-based processors, known for their lower power consumption compared to traditional x86 processors, are becoming increasingly popular in data centers. These processors provide a good balance of performance and efficiency, making them suitable for AI workloads. 

Additionally, distributed computing using application-specific integrated circuits (ASICs) and system-on-chip (SoC) processors handle their specialized functions far more efficiently than general-purpose processors. Finally, even GPUs have become specialized. Certain GPUs optimized for AI tasks, such as NVIDIA’s A100, offer greater performance per watt, enhancing energy efficiency.

Dynamic Workload Management

Intelligent workload management can optimize the use of computational resources. By dynamically allocating tasks based on current energy availability and efficiency metrics, data centers can ensure that high-energy tasks are performed during periods of low demand or when renewable energy sources are most available. This approach helps balance performance needs with sustainability goals.

Green Data Center Practices

Investing in green data center technologies can mitigate the environmental impact of AI. Advanced cooling solutions, renewable energy sources and efficient power management systems can significantly reduce the energy footprint of data centers. For example, using natural cooling methods and investing in renewable energy sources like solar or wind can lower reliance on fossil fuels and decrease greenhouse gas emissions.

Related ReadingAI Has a Climate Change Problem

 

How AI Can Use Energy More Efficiently

Data center equipment suppliers and operators constantly improve efficiency to mitigate the impact of the growing processing demand on the overall energy demand. Emerging technologies offer promising contributions to satiate the energy appetite of AI infrastructure.

Computational Storage-Enabled SSDs

Computational storage integrates processing capabilities directly into storage devices. This localization of storage-specific data processing minimizes latency and significantly lowers energy consumption. By performing computations on the drive itself, computational storage drives (CSDs) can accelerate data processing and reduce the energy required for these operations. 

Additionally, by scoping the processing capability to align with the individual drive performance and power limits, CSDs allow for 1:1 scaling of compute and storage performance to enhance efficiency and provide for more granular scaling of power consumption.  ScaleFlux’s SSDs, for example, offer intelligent computational storage technology that optimizes the data pipeline, enhancing scalability and efficiency.

Distributed Computing

Distributed computing involves spreading computational tasks across multiple nodes or devices, optimizing resource usage and reducing the burden on individual data centers. This decentralized approach allows for more efficient workload management, which reduces overall energy consumption. By leveraging a network of less energy-intensive computational resources, distributed computing can reduce data movement and better balance power consumption with the work being done.

Compute Express Link (CXL)

One of the biggest challenges for efficiently scaling AI performance is the so-called memory wall – the gap between the GPU cores’ processing capabilities and the capacity and bandwidth of memory technology that feeds data to the GPUs. 

CXL technology enhances data processing efficiency by enabling faster communication between CPUs, GPUs, and memory. This expanded memory capability reduces latency and improves data access speeds, leading to more efficient processing and lower energy consumption. CXL optimizes the data pipeline between storage, memory, and computational units, significantly enhancing performance while maintaining energy efficiency.

Putting AI on a power diet is necessary for sustainable growth. By understanding the primary drivers behind AI’s energy consumption, adopting strategies to optimize efficiency and introducing innovative technologies, companies can balance the need for AI-driven innovation with the imperative of sustainability.

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