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As we approach the end of 2024 and look back at the technological advancements it has brought, the buzz surrounding artificial intelligence and high-performance computing continues to overwhelm all other web3 developments. Therefore, this year has seen overwhelming customer demand for AI products, putting even more pressure on data centers to deliver AI infrastructure to increase efficiency.
As companies race to adopt these technologies, many have considered investing in computing resources such as graphics processing unit chips commonly used to train AI models, blockchain, self-driving cars, and other emerging applications. But before organizations can fully embrace the exciting potential of this hardware, we must carefully consider the complexities and challenges that come with it.
It’s true that the possibilities of AI are indeed fascinating. Check out OpenAI’s ChatGPT statistics, which have over 200 million active users each week. From automating routine tasks to driving sophisticated analytics, the potential of AI and large-scale language models is enormous and these technologies are here to stay.
Growth has just begun
Naturally, organizations are striving to gain a competitive advantage through AI, leading major players like Meta and Apple to invest in software that supports this technology.
According to a recent report from management consulting firm Bain & Company, AI workloads are expected to grow 25 to 35 percent annually over the next few years, growing the AI-related hardware and software market size to $780 billion to $990 billion by 2027. It works. .
But investing in computing resources requires more than simply purchasing hardware or signing up for a cloud service. When evaluating some of the barriers to investing in this software, one of the biggest hurdles investors face is the initial cost.
High-end GPUs like NVIDIA’s A100 or H100 can cost millions of dollars or more, and can incur additional costs for the power needed to power servers, cooling systems, or devices. This presents a difficult challenge for retail investors looking to add this technology to their portfolios, often limiting investment opportunities in strong companies.
Aside from the hefty price tag, the hardware itself is not for the faint of heart. A thorough understanding of how to effectively optimize and manage these resources is required. Investors must have specialized knowledge of hardware and software, and technical expertise is a prerequisite.
Even if economic and technical issues are not impediments to investment, serious obstacles still remain. It’s either supply or shortage. A Bain & Company report found that demand for AI components could exceed supply capacity and grow by more than 30%.
Investing in computing may seem impossible, but there are new models that are making computing more accessible to ordinary investors, allowing them to harness the potential of advanced computing despite existing barriers.
Tokenization as a Solution
Exabits provides users with the opportunity to become stakeholders in the AI computing economy through the tokenization of high-performance GPU resources, allowing them to earn rewards and profits without having to manage the complexities of hardware ownership. With a low-cost entry point and reward system, Exabits makes investing in AI computing easier by allowing individuals to participate in the demand for GPU resources while avoiding the risks associated with direct investments.
Exabits has created a business model called “Four Seasons of GPUs” that emphasizes quality assurance and consistency across GPU products. Just as Four Seasons is world-renowned for its high service levels, the “Four Seasons of GPUs” provides quality-assured hardware that investors can trust. Investors can receive personalized support through Exabits, similar to a hotel’s commitment to customer satisfaction. As a platform and business, Exabits aims to provide equal opportunities for investors to participate in the growing AI computing economy.
As demand for computing grows, so does the appetite for investment opportunities in this fast-emerging field. As AI, blockchain, and other technology trends continue to grow, the future of GPU development will depend on the industry’s ability to meet this demand and create opportunities to continue to expand access to this respected technology.