The AI compute craze for retail investors in web3

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As we approach the end of 2024 and reflect on the technological advances it brings, excitement around artificial intelligence and high-performance computing continues to overshadow all other web3 developments. Therefore, this year has seen huge customer demand for AI products and even greater pressure on data centers to deliver AI infrastructure to improve efficiency.

Many of the companies racing to adopt these technologies have considered investing in computing resources such as graphics processing unit chips, which are commonly used to train artificial intelligence models, blockchains, autonomous vehicles, and other emerging applications. But before organizations fully embrace the exciting potential of this hardware, we need to carefully consider the complexities and challenges that come with it.

It’s true that the promises of artificial intelligence are indeed enticing. Just look at the statistics of OpenAI ChatGPT, which aggregates more than 200 million weekly active users. From automating mundane tasks to powering complex analytics, the potential of AI and big language models is huge and these technologies are here to stay.

Growth has just begun

Unsurprisingly, organizations are keen to gain competitive advantage through AI, leading major players such as Meta and Apple to invest in software that supports this technology.

A recent report from Bain & Company, a management consulting firm, found that AI workloads are expected to grow at an annual rate of 25 to 35 percent over the next few years, and the market for AI-related hardware and software will reach between $780 billion and $990 billion by 2027. revealed that it was expected to appear. .

But investing in computing resources requires more than just purchasing hardware or subscribing to a cloud service. If we consider some of the obstacles to investing in this software, one of the biggest obstacles investors face is the startup cost.

Costs for NVIDIA’s advanced GPUs, such as the A100 or H100, can run into millions of dollars, with additional costs for servers, cooling systems, or electricity needed to power devices. This poses a challenge for retail investors looking to add this technology to their portfolios, often limiting investment opportunities to strong companies.

Beyond the hefty price tag, the hardware itself isn’t for the faint of heart. This requires a comprehensive understanding of how to effectively optimize and manage resources. Investors must have specialized knowledge of hardware and software, making technical expertise a prerequisite.

Even if affordability and technical difficulties are not obstacles to investment, a significant obstacle remains: supply, or lack thereof. The Bain & Company report finds that demand for AI components could grow by 30 percent or more, outpacing supply capabilities.

While investing in IT may seem out of reach, there are new models that make IT more accessible to ordinary investors and allow them to tap into the potential of advanced IT despite existing barriers.

Tokenization as a solution

Through the tokenization of high-capacity GPU resources, Exabits offers users the opportunity to become stakeholders in the AI ​​computing economy, allowing them to earn rewards and revenue without having to manage the complexity of hardware ownership. With affordable entry points and reward systems, Exabits makes investing in AI computing more accessible by allowing individuals to participate in the demand for GPU resources while avoiding the risks associated with direct investment.

Exabits has created the “Four Seasons of GPU” business model, which emphasizes quality assurance and consistency in GPU offerings. Just as Four Seasons is known worldwide for its high standards of service, the “Four Seasons of GPU” provides quality-assured 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 business, Exabits aims to provide equal opportunities for investors to participate in this growing AI computing economy.

As the demand for computing increases, so does the appetite for investment opportunities in this rapidly developing field. With the continued growth of artificial intelligence, blockchain, and other technology trends, the future of GPU development will depend on the industry’s ability to meet these demands and create opportunities that continue to expand access to this respected technology.