Can Hardware Development Keep Up with AI’s Skyrocketing Capabilities?




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(Photo : Gerd Altmann from Pixabay)

There’s no denying that the field of artificial intelligence (AI) has witnessed an unprecedented explosion in capabilities—particularly in the realm of large language models (LLMs)—over the last couple of years. To this point, the introduction of groundbreaking models like GPT-4, Claude 3, and Google’s Gemini has marked a significant leap in performance, scalability, and versatility, pushing the boundaries of what’s possible with this technology.

For instance, GPT-4, launched by OpenAI during Q4 2022, exhibited the capacity to process 1.75 trillion parameters—elements that an AI assimilates from the training data to make projections—maintaining its position as one of the most powerful LLMs available in the market, one that is capable of performing diverse tasks (ranging from language translation to code generation).

Similarly, Anthropic’s Claude 3 models, unveiled in 2024, marked a significant leap forward in AI capabilities. Claude 3.5 Sonnet, for example, combines advanced reasoning skills with a broad knowledge base and strong coding abilities. Furthermore, it excels in understanding nuanced language, appreciating humor, and following complex instructions, making it particularly well-suited for tasks demanding high-quality natural language processing and generation.

Lastly, Google’s Gemini LLM, which emerged in 2023, offers unparalleled performance in handling multimodal data, including text, images, audio, and video. In fact, it currently supports a massive context length of up to 1 million tokens—equivalent to 3–7 books—allowing for the processing and generation of lengthy and complex inputs.

When Ambition Outpaces Infrastructure

While these AI models have undoubtedly demonstrated remarkable capabilities, they have also highlighted a growing concern: the immense computing power required to train and run them. The hardware demands of these sophisticated AI systems are staggering, and only a handful of corporations possess the resources to build and maintain the necessary infrastructure.

This hardware bottleneck is already creating challenges for the development of new AI models, especially those with significant social value but unclear business cases. Smaller research teams and startups often find themselves priced out of the AI race, unable to access the computing resources needed to train and deploy large-scale models.

Moreover, the environmental impact of these power-hungry AI systems is becoming increasingly apparent. According to recent data, AI’s electricity demand is forecast to surge exponentially. Morgan Stanley estimates that global data center power use will triple this year, from ~15 TWh in 2023 to ~46 TWh in 2024. This coincides with the ramp of new AI chip deployments and increased utilization of existing GPUs.

Generative AI power consumption and demand
Generative AI power consumption and demand (source: Forbes)

Therefore, experts believe that the solution to this growing challenge may lie in distributed computing models. By leveraging the collective power of numerous smaller systems, a scalable and more accessible infrastructure for AI development can be created. One company at the forefront of this approach is Qubic.

Founded by Sergey Ivancheglo (also known as Come-from-Beyond), Qubic is pioneering a unique form of decentralized autonomous organization (DAO) that combines blockchain technology with AI. At its core, the project is powered by 676 Computors responsible for executing smart contracts, ensuring reliability through a quorum-based system.

What sets Qubic apart is its Useful Proof-of-Work (uPoW) system, which leverages mining capacities for AI training. This innovative approach not only addresses the hardware bottleneck but also democratizes access to AI technologies. By distributing the computational load across a network of miners, Qubic creates a more scalable and efficient infrastructure for AI development.

Furthermore, Qubic’s architecture is designed to achieve remarkable performance, with reports of reaching 40 million transactions per second (TPS) on a live test. This high throughput, combined with features like instant finality and no blockchain reorganizations, positions Qubic as a potential game-changer in the realm of decentralized AI infrastructure. 

Lastly, the platform’s community-driven approach, with no pre-mining or venture capital involvement, ensures that its development has continued to align closely with its user’s needs and the broader vision of democratizing AI access and development.

The Future of AI Should Be Distributed and Democratized

As we look to the future, it’s clear that AI is here to stay. Bloomberg Intelligence projects that the generative AI market could grow to $1.3 trillion over the next ten years from a market size of just $40 billion in 2022, expanding at a compound annual growth rate (CAGR) of 42%. Experts believe this growth will be driven by training infrastructure in the near term and gradually shift to inference devices for large language models, digital ads, specialized software, and services.

Given this projected growth, the need for sustainable and accessible AI infrastructure stands to become even more critical. Distributed computing models, like the one employed by Qubic, offer a promising solution to this challenge. By harnessing the power of decentralized networks, these models can provide the necessary computational resources while also distributing the benefits of AI more broadly.

The advantages of this approach extend beyond mere scalability. Decentralized AI systems can potentially reduce the concentration of power in the hands of a few tech giants, fostering a more diverse and innovative AI ecosystem. They can also provide a more energy-efficient alternative to centralized data centers, helping to mitigate the environmental impact of AI’s rapid growth.

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* This is a contributed article and this content does not necessarily represent the views of techtimes.com


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