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8 Ways that LLMs and Generative AI are Changing Hardware 8 Ways that LLMs and Generative AI are Changing Hardware
With large language models and generative AI reshaping the digital landscape, what isn’t talked about enough is how these technologies have... 8 Ways that LLMs and Generative AI are Changing Hardware

With large language models and generative AI reshaping the digital landscape, what isn’t talked about enough is how these technologies have also revolutionized and significantly shifted the landscape of hardware requirements. As these sophisticated AI models become more integral to a myriad of applications—from natural language processing to content creation and beyond—the underlying hardware infrastructure has had to evolve rapidly to keep pace. This transformation is evident in several key areas, highlighting the profound impact of LLMs and generative AI on hardware development and energy consumption. In this article, we’ll take a look at how the AI hardware game has changed, what new demands have arisen, and where the industry is moving toward.

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The Surge in Energy Demand for AI Chips

If you’re outside of the programming aspect of AI, you are likely to realize how power-hungry these programs can get. That’s because the computational power required to train and run LLMs and generative AI is immense. AI chips are designed specifically to handle the complex calculations of machine learning algorithms efficiently. But to do this effectively, they are now consuming more energy than ever before. This increase is due to the sheer volume of data being processed and the complexity of the models. 

Traditional CPUs and even GPUs, while still crucial, are being augmented or replaced by more specialized hardware like TPUs (Tensor Processing Units) and FPGAs (Field-Programmable Gate Arrays) which offer higher performance but at a cost of requiring more energy. This shift necessitates not only a reevaluation of energy sources but also a significant upgrade in power delivery and cooling infrastructure to support these high-performance chips.

Legacy Data Centers Falling Behind

Because of this shift in computing/energy consumption, legacy data centers, originally designed to support traditional computing workloads, are increasingly unable to meet the demands of LLMs and generative AI. These models require not just more computational power but also higher-speed interconnects and more sophisticated cooling systems. The limitations of legacy infrastructure in terms of power density (the amount of power used per square foot) and cooling efficiency are becoming apparent, prompting a shift towards modernizing data centers or building new ones that are specifically designed to accommodate the needs of high-powered AI computations.

The Rising Demand for High-Power AI Centers

As a direct consequence of the escalating hardware demands, there’s a growing need for high-power AI research centers. These facilities are equipped with state-of-the-art hardware capable of supporting the enormous computational and energy requirements of LLMs and generative AI. High-power AI centers are becoming pivotal in the development and training of advanced models, necessitating significant investment in both the physical infrastructure and the energy resources needed to sustain them.

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The Necessity of Liquid Cooling

To address the heat generated by the increased power consumption, liquid cooling systems are becoming a necessity in AI hardware setups. Unlike traditional air cooling, liquid cooling can more efficiently manage the heat output of high-performance chips, maintaining optimal operating temperatures and improving both the performance and longevity of the hardware. This cooling method, once considered exotic and used only in high-performance computing (HPC) or gaming rigs, is now becoming mainstream within data centers to support the thermal management of AI workloads.

Custom Hardware Needs

Because of all of these new hardware needs pressuring the market, specifically the needs of ever-growing LLMs, this is a push to the development of custom hardware solutions. Companies like Google, NVIDIA, and smaller startups that can create specialized hardware plans for individual clients, are designing custom chips (e.g., TPUs, the A100 GPU) that are optimized for AI workloads, offering better performance for these tasks compared to general-purpose processors.

Networking Infrastructure

Like with everything on the internet, there is a growing need to transfer large datasets and model parameters across the globe for AI model training and deployment. This is driving innovations in networking infrastructure not seen in years. Some of the changes include faster internet technologies, such as 5G and beyond, and improvements in data center network architecture to support the bandwidth and latency requirements of distributed AI computations, and requirements in hardware to improve ping between users and software housed in far-off data centers.

Calls for Renewable Energy Integration

But this isn’t going unnoticed. The environmental impact of running LLMs and Generative AI at scale is not trivial. The increased energy consumption of AI chips and the infrastructure to support them has led to more calls for the integration of renewable energy sources into the infrastructure as a means to offset the impact of power consumption and the use of rare earth materials. 

This is applying growing pressure on industries scaling technology with AI to mitigate their carbon footprint by adopting green energy solutions. Some of this involves not only sourcing energy from renewable resources but also innovating in energy efficiency at the hardware level to ensure that the advancements in AI technology are sustainable in the long term.

Quantum Computing

Believe it or not, generative AI and LLMs are having a significant impact on quantum computing. While still in its early stages, quantum computing can possibly be seen as the next avenue for high-level model training and problem-solving. If this can be done, it could revolutionize the level and scale of model training in the medium to long term. This is especially important as quantum computers have different hardware profiles of requirements. This will become even more important as Google, Meta, and others continue their push for AGI in the future. 

Conclusion on AI Hardware

Overall, as AI-focused hardware and generative AI technology continue to scale across industries, the impact of their entrance into the market will grow. Legacy data centers, concerns about the environment, and power consumption are driving innovators to find new ways to mitigate the negative impacts of AI. Much of which falls under the sub-field of responsible AI. There are many solutions being discussed, but only time will tell which are realistic and scaleable. 

If you’re interested in this topic, and the overall topic of responsible AI, then you’ll want to check out ODSC East 2024 this April 23-25. At East, there’s an entire track filled with talks, workshops, and networking events focused on responsible AI

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ODSC Team

ODSC Team

ODSC gathers the attendees, presenters, and companies that are shaping the present and future of data science and AI. ODSC hosts one of the largest gatherings of professional data scientists with major conferences in USA, Europe, and Asia.

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