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As we approach the end of 2024 and reflect on the technological advancements it brought, the buzz surrounding artificial intelligence and high-performance computing continues to overshadow all other web3 developments. This year saw overwhelming customer demand for AI products and even greater pressure on data centers to deliver AI infrastructure to boost efficiency.

AI’s Growing Influence

With companies racing to adopt these technologies, many have considered investing in compute resources like graphic processing unit chips, commonly used for training AI models, blockchains, autonomous vehicles, and other emerging applications. However, before organizations fully embrace the potential of this hardware, it’s essential to consider the complexities and challenges that come with it.

The promise of AI is indeed enticing. Just look at the stats from OpenAI’s ChatGPT, which garners over 200 million active weekly users. From automating mundane tasks to driving sophisticated analytics, the potential of AI and large language models is vast and here to stay.

Investment Landscape

Organizations are eager to gain a competitive edge through AI, leading major players like Meta and Apple to invest in the software that supports this technology. A recent report from Bain & Company revealed that AI workloads are expected to grow 25 to 35 percent annually over the next several years, pushing the AI-related hardware and software market to between $780 billion and $990 billion by 2027.

However, investing in compute resources involves more than just purchasing hardware or subscribing to a cloud service. One of the biggest hurdles investors face is the initial cost. Advanced GPUs like NVIDIA’s A100 or H100 can cost millions of dollars, with additional expenses for servers, cooling systems, and electricity. This presents a challenge for retail investors looking to add this technology to their portfolios, often limiting opportunities to powerful corporations.

Technical and Supply Challenges

Beyond the hefty price tag, the hardware itself requires a thorough understanding of optimizing and managing these resources effectively. Investors need specialized knowledge in hardware and software, making technical expertise a prerequisite.

Even if affordability and technical challenges weren’t barriers, supply issues remain significant. The Bain & Company report indicates that demand for AI components could grow by 30 percent or more, outpacing supply capabilities.

Making AI Compute Accessible

While investing in compute may seem out of reach, new models are making it more accessible to everyday investors, allowing them to tap into the potential of advanced computing despite existing barriers.

Tokenization as a Solution

Through the tokenization of high-compute GPU resources, platforms like Exabits offer users an opportunity to become stakeholders in the AI compute economy. This allows them to earn rewards and revenue without managing the complexities of hardware ownership. With affordable entry points and reward systems, Exabits enables individuals to participate in the demand for GPU resources while avoiding the risks associated with direct investment.

Exabits has coined its business model, “The Four Seasons of GPU,” emphasizing quality assurance and consistency across its GPU offerings. Just as the Four Seasons is renowned for its high service standards, “The Four Seasons of GPU” provides quality-guaranteed hardware that investors can trust. Investors can rely on Exabits for personalized assistance, similar to the hotel’s commitment to customer satisfaction. As a platform and a business, Exabits aims to provide equal opportunities for investors to participate in the growing AI compute economy.

As demand for computation rises, so does the appetite for investment opportunities within this rapidly emerging space. With the ongoing growth of AI, blockchain, and other tech trends, the future of GPU development will depend on the industry’s ability to meet these demands and create opportunities that continue to broaden access to this esteemed technology.

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