October 1, 2024
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Scaling AI isn't the real problem now, it's the need to adopt sustainable AI solutions. Why? Well, we see it happening every day. Leading tech giants like Meta and OpenAI release large-scale AI models with billions of parameters (which is great) but do we truly understand its environmental cost? The growing carbon footprint, energy consumption and operational costs are all a part of this problem.
In our latest article, we will discuss how the environmental impact of large-scale AI is a potential threat to growing businesses and how NexGen Cloud is helping mitigate these risks.
How Large-Scale AI Impacts the Environment
The environmental implications of AI are largely tied to the energy-intensive nature of training and deploying large-scale AI models.
Generative AI’s Large Carbon Footprint
A recent study by Hugging Face and Carnegie Mellon University researchers found that a significant carbon footprint is associated with generative AI models, particularly in tasks that create new content. These include text generation, summarisation, image captioning, and image creation, with image-related tasks being notably more energy- and carbon-intensive than text-focused tasks. For example, generating 1,000 images with a model like Stable Diffusion XL produces as much carbon dioxide as driving a gasoline-powered car for 4.1 miles. In contrast, the least carbon-intensive text generation models emit carbon equivalent to just 0.0006 miles driven, making image generation 6,833 times more polluting.
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Image source: Research Paper (Power Hungry Processing: Watts Driving the Cost of AI Deployment)
Decoder-only models particularly for tasks with longer outputs demand more energy and generate higher carbon emissions when compared to sequence-to-sequence models. While training generative models is far more energy and carbon-intensive than inference, training is often perceived as a one-time cost. However, for widely deployed models the cumulative energy consumption from millions of inferences can quickly match or exceed the emissions from training.
This growing carbon footprint is concerning as generative AI scales up in user-facing applications, where millions of users generate vast amounts of content daily. The environmental impact raises questions about AI advancements' sustainability and underscores the need for energy-efficient model designs and deployment strategies.
Similar Read: How to Scale LLMs with the AI Supercloud
Data Centres and Cooling Systems
Data centres that support AI applications are a major source of carbon emissions. The International Energy Agency (IEA) reports that in 2022, data centres consumed between 240 and 340 terawatt-hours (TWh) of electricity, representing up to 1.3% of global electricity use. With the rising demand for AI, high-performance computing, and cloud services, energy consumption by data centres is expected to continue growing.
Cooling systems significantly contribute to data centre emissions due to their heavy electricity use and resulting heat dissipation. By 2026, electricity consumption by data centres in the AI sector could double, with global electricity use by data centres projected to surpass 1,000 TWh, up from an estimated 460 TWh in 2022. Data centres are thus driving up electricity demand globally.
Energy Usage in AI Inference
While training AI models is the most energy-demanding stage of development, inference also plays a substantial role in energy consumption. As AI applications become increasingly widespread, the volume of inferences made globally continues to grow. For example, OpenAI's GPT-3 with its 175 billion parameters consumed 1,287 megawatt-hours of electricity and produced 552 tons of CO2 equivalent [see source], comparable to the annual emissions of 123 gasoline-powered cars, and mind you this was just for getting the model ready before releasing to the public. Now that the model is deployed across millions of devices, the cumulative energy usage from repeated inferences contributes to escalating electricity consumption and environmental impact over time.
However, model size alone does not determine carbon emissions. For example, the BLOOM model developed by the BigScience project in France is similar in size to GPT-3 but has a much smaller carbon footprint, using 433 MWh of electricity and emitting 30 tons of CO2eq [see source]. In fact, research by Google [see source] shows that models of similar size can achieve a 100 to 1,000-fold reduction in emissions by employing more efficient architectures, advanced processors, and eco-friendly data centres. Read on to discover how we focus on sustainability and energy efficiency.
Similar Read: How AI Supercloud Accelerates Large AI Model Training
Why Addressing Sustainability is Crucial for Large-Scale AI Adoption
Sustainability isn’t just a moral imperative, it’s a business necessity. Environmental regulations, rising energy costs and consumer awareness are pushing companies to adopt greener technologies. Without addressing these concerns, organisations can risk:
- Higher operational costs due to energy inefficiency. Sustainable technologies often lead to better operational efficiency. For example, energy-efficient hardware and renewable energy sources can reduce long-term costs while improving performance.
- Reputation damage in an increasingly eco-conscious market. Modern consumers are increasingly conscious of their environmental impact. A survey by PwC found that 83% of consumers believe that companies should actively contribute to ESG best practices. For AI companies, adopting green practices can be a competitive differentiator.
- Regulatory penalties as governments introduce stricter environmental policies. Governments worldwide are enacting stringent regulations to curb carbon emissions. For example, the European Union's Green Deal aims to achieve climate neutrality in Europe by 2050, reducing emissions by 55% by 2030. This could directly affect companies adopting large-scale AI operations within the region.
Similar Read: Overcoming the Challenges of Large-Scale Machine Learning
How NexGen Cloud Is Building a Greener Future for AI
NexGen Cloud recognises the environmental challenges posed by large-scale AI and offers innovative solutions to mitigate them.
100% Renewably Powered Infrastructure
We exclusively partner with Tier-3 data centres powered entirely by renewable energy, including solar, wind, and hydroelectric power. These facilities provide reliable, sustainable energy for AI workloads while significantly reducing carbon emissions. By leveraging green energy, we empower businesses to deploy AI models without contributing to fossil fuel consumption.
Energy Efficient Hardware
Our infrastructure on the AI Supercloud integrates the latest GPU hardware including the NVIDIA GB200 NVL/72, optimised for energy efficiency. The hardware we offer delivers exceptional performance with minimal power consumption. Our AI Supercloud uses liquid cooling systems that minimise energy usage, ensuring businesses can scale AI projects sustainably.
Partnerships for a Greener Future
We work with LEED-certified Tier-3 data centres that leverage state-of-the-art cooling technologies and energy management systems. These partnerships enable us to create AI environments with a reduced environmental footprint, providing businesses with the perfect balance of sustainability and performance.
Data Sovereignty and Security
We prioritise sustainability alongside data sovereignty and security. By operating in trusted data centres across Europe and Canada, we ensure compliance with local regulations while minimising the energy impact of data transfers.
Conclusion
The scope for AI will never end and you will know because the Global Market for AI is expected to reach USD 826.70 Billion by 2030. Businesses cannot afford to miss out on using AI in their operations. At the same time, it's important to stay competitive by adopting sustainable AI solutions. By choosing NexGen Cloud, your business can:
- Reduce its carbon footprint.
- Achieve cost savings through our energy-efficient technologies.
- Align with consumer and regulatory expectations for sustainability.
The future of AI is not just about what we can achieve but how responsibly we achieve it. NexGen Cloud is leading the way, proving that innovation and sustainability can go hand in hand.
Book a call with our solutions engineer to explore tailored solutions for your needs and learn more about our renewable-powered infrastructure.
FAQs
What is the environmental impact of large-scale AI models?
Large-scale AI models, particularly generative AI, consume significant energy, leading to a substantial carbon footprint due to the high demands of training and inference.
How does NexGen Cloud help reduce the environmental impact of AI?
NexGen Cloud uses 100% renewable energy-powered data centres and energy-efficient hardware to minimise the environmental footprint of AI operations.
Why is sustainability important for AI companies?
Sustainability is crucial to reduce operational costs, improve efficiency, enhance brand reputation, and comply with increasing environmental regulations.
How does the AI Supercloud ensure energy efficiency in its AI infrastructure?
The AI Supercloud integrates advanced, energy-efficient GPU hardware and liquid cooling systems to optimise performance with minimal power consumption.
What are the benefits of adopting sustainable AI solutions?
Adopting sustainable AI solutions helps businesses reduce their carbon footprint, lower energy costs and align with both consumer expectations and regulatory requirements.
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