Generative AI fuels compute demand: Urgency for sustainable solutions
Generative AI: Amongst the Top 10 Tech Developments of 2023.
Every corner in the world, has incoroporated some kind of Generative AI in their workflow in order to leverage their work. Be it a Language Model or text-to-visual contents, the underline tech is Gen AI.
Shortly after GPT-3.5 release, a huge crowd had shifted their mindset from being cognizant about AI to actually using it to enhance the quality of work, replacement to non-AI tools, and other different purposes. All these has happened in a span of 2 yrs and from there the demand is sky rocketing.
Open AI’s Sora is a massive jump to previous versions of text-to-video and some studies have reported that Sora will set a new benchmark for Generative AI. The quest to develop an exceptionally scalable, efficient, and flawless Generative AI model has only just begun.
Let’s slide to the main part, *COMPUTE POWER*
The increase in demand for AI and next-gen AI applications brings along a need for more computing power. But it’s important for leaders in the semiconductor industry to grasp where this demand is coming from and how next-gen AI will be used. It’s not just about what regular consumers want. In both industrial and consumer markets, the demand for next-gen AI can be split into two main parts: training and inference. Training involves handling large amounts of data and using a lot of computing power. On the other hand, inference doesn’t need as much computing power for each use case.
In an recent report by Mckinsey & Co, they estimate compute demand to reach over 25,000 ZettaFLOPs by 2030. Mostly to be consumed by B2C & a 30% direct demand from B2B.
Building a Gen AI tool for consumer applications is seen as the best approach, as it’s expected to outshine B2B applications by a large margin.
Also, semiconductor leaders need to think about how much computing power generative AI will need. They also need to be ready to change the hardware and infrastructure, like data centers, servers, and computer chips.
The Sustainability Debate
In US, Climate and Environmental Justice have raised concerns regarding the increasing adoption of AI and its implications on the environment.
Overlooking the fact, it could pose a real danger to environments.
One aspect to consider is the carbon footprint associated with the massive data centers required to support the computational needs of AI training and inference. These data centers consume vast amounts of electricity, often sourced from non-renewable energy sources, leading to significant greenhouse gas emissions.
Moreover, the manufacturing process of the hardware components used in these data centers also contributes to environmental degradation, with concerns over e-waste disposal and resource depletion. Therefore, OEM’s (Original Equipment Manufacturer) are always targeted when it comes to environmental debates
Addressing these sustainability challenges requires a multifaceted approach. One potential solution lies in the development of more energy-efficient hardware architectures specifically architectured for AI workloads. This could involve innovations in chip design, cooling systems, and power management techniques to minimize energy consumption without compromising performance.
Additionally, there’s a growing emphasis on transitioning towards renewable energy sources to power data centers, reducing their carbon footprint and reliance on fossil fuels. Initiatives such as utilizing solar, wind, or hydroelectric power can significantly mitigate the environmental impact of AI compute infrastructure.
Optimizing AI algorithms and models to be more computationally efficient can be a major contributor to reducing overall energy consumption. Techniques such as model compression, quantization, and sparsity regularization aim to streamline the computational workload without sacrificing accuracy, thereby minimizing resource utilization.
“Sustainable computing isn’t just important, it’s essential”
