The role of photonics in sustainable AI development

Generative AI tools are heralding a new era of productivity and creativity. Across industries, organisations are using tools like ChatGPT to support their everyday work. This empowers employees to focus their resources on tasks that can’t be automated and require human input. But it’s becoming increasingly clear that generative AI tools have an insatiable hunger for energy.

The data centres powering AI tools require significant amounts of energy to train models, as well as service user requests. And the rapid increase in consumption is already evident. In 2022 globally, data centres used 460 terawatt hours of electricity, yet by 2026 the International Energy Agency (IEA) are forecasting that this will double to 1,000 terawatt hours. That will be close to four times the UK’s annual electricity consumption. The biggest challenge facing the AI industry today is how can growth be maintained sustainably.

The outdated infrastructure obstacle

The rate of innovation in AI in the last five years has been immense. In the space of a few years, AI tools have progressed from novelty and experimental to ingrained in our everyday lives. Take internet search as an example, our questions are often answered in AI summaries before we even click on a link. However, the infrastructure behind these tools has not developed at the same pace. Many data centres were constructed before the AI boom and weren’t designed with power-hungry modern GPUs in mind.

The historical approach of incremental fixes and upgrades is no longer fit for purpose. This method cannot support the demand of current innovation and continuing with it will further expose the inefficiencies of ageing digital networks. But many businesses cannot afford the financial, environmental, and spatial costs that running many state-of-the-art GPUs demands. To overcome this challenge, businesses should look towards All-Photonics Networks (APNs).

APNs and remote GPU services

Contrary to traditional electronic networks, APNs utilise much faster and more energy efficient photonic data transmission. Crucially, this technology enables remote processing, meaning workloads can be processed in modern data centres that are built for purpose. These data centres are powered by renewable energy sources and employ sophisticated cooling systems to reduce any environmental impact. Ultimately, APNs facilitate training future AI models at greater speeds and with reduced power consumption.

Remote GPU services remove the requirement for large on-site GPU stacks. By enabling organisations to outsource their AI training needs to specialised data centres, APNs allow businesses to retain a competitive edge by investing in AI innovation, while simultaneously cutting power consumption, operational costs, and making the entire process more sustainable. Opting to use remote GPU services over traditional infrastructure makes sure that workloads are being processed in the optimal locations.

Widening top-tier GPU accessibility

APNs boast latency and bandwidth benefits over traditional electronic networks. Using APN-powered training is vastly more efficient. Organisations can do more for significantly less and in much less time. While traditional methods for AI training may have required weeks or months to complete, leveraging APNs for training opens the possibility of training models in mere days. Therefore, cutting costs and reducing waste. And what’s good for the planet, is also good for business.

Remote GPU access makes the technology accessible for more businesses than before. While previously top-tier tech was only attainable for the largest organisations, smaller organisations will soon also be able to access the most powerful AI compute resources. Remote access removes the need for enormous infrastructure investment, democratising the resource for organisations large and small. APNs hold the key to levelling the playing field and improving market competition.

And there needn’t be any negative impact on security. As AI models utilise huge datasets commonly including private information, any infrastructure changes – such as the adoption of APNs – must uphold the established standards of end-to-end encryption, remote attestation and advanced security measures to ensure that training data remains protected.

Laying the foundations for a more sustainable future

APNs offer a compelling solution to the escalating energy demands of AI. By enabling remote access to optimised data centres powered by renewable energy, APNs reduce the environmental footprint of AI development. This innovative approach facilitates faster training, lower operational costs, and wider accessibility to cutting-edge GPU resources.

Ultimately, APNs empower businesses to embrace AI’s transformative potential while prioritising sustainability. This shift towards energy-efficient infrastructure is crucial for responsible AI development, ensuring continued innovation while mitigating the environmental impact and fostering a more sustainable technological landscape.