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As we approach the end of 2024 and reflect on the technological advancements it has brought, the buzz around artificial intelligence and high-performance computing continues to overshadow all other Web3 developments. So, this year has seen overwhelming customer demand for AI products and even greater pressure on data centers to provide AI infrastructure to improve efficiency.
As companies rush to adopt these technologies, many have considered investing in compute resources such as graphics processing unit chips, commonly used for training AI models, blockchains, autonomous vehicles and other emerging applications. But before organizations fully realize the exciting potential of this hardware, we need to carefully consider the complexities and challenges that come with it.
It is true that the promises of AI are tempting. Just look at the stats for OpenAI’s ChatGPT, which boasts over 200 million weekly active users. From automating mundane tasks to conducting sophisticated analyses, the potential of AI and large language models is vast, and these technologies are here to stay.
The growth has just begun
Unsurprisingly, organizations are eager to gain a competitive advantage through AI, which has led major players like Meta and Apple to invest in software that supports the technology.
A recent report from Bain & Company, a management consulting firm, found that AI workloads are expected to grow 25% to 35% annually over the next few years, pushing the market for related hardware and software to AI between 780 and 990 billion dollars by 2027. .
However, investing in IT resources involves much more than simply purchasing hardware or subscribing to a cloud service. If we evaluate some of the obstacles to investing in this software, one of the biggest obstacles that investors face is the initial cost.
Costs for advanced GPUs like NVIDIA’s A100 or H100 can run into the millions of dollars, with additional costs for servers, cooling systems, or the electricity needed to power the devices. This presents a challenge for retail investors looking to add this technology to their portfolios, often limiting investment opportunities to powerful companies.
Beyond the high price, the hardware itself is not for the faint of heart. This requires a deep understanding of optimizing and effectively managing these resources. Investors must have specialized knowledge in hardware and software, making technical expertise a prerequisite.
Even if affordability and technical challenges were not barriers to investment, a significant obstacle remains: supply or lack thereof. The Bain & Company report reveals that demand for AI components could grow by 30% or more, outpacing supply capabilities.
Although investing in IT may seem out of reach, there are new models that make it more accessible to ordinary investors, allowing them to harness the potential of advanced IT despite existing obstacles.
Tokenization as a solution
Through the tokenization of high-computing GPU resources, Exabits offers users the opportunity to become participants in the AI compute economy, allowing them to earn rewards and revenue without having to manage the complexities of ownership material. With affordable entry points and reward systems, Exabits allows individuals to participate in the demand for GPU resources while avoiding the risks associated with direct investment, making investment in AI computing more profitable. accessible.
Exabits invented its business model, “The Four Seasons of GPU,” with a focus on quality assurance and consistency of its GPU offerings. Just as the Four Seasons is globally recognized for its high service standards, “The Four Seasons of GPU” provides quality-assured hardware that investors can trust. Investors can count on Exabits for personalized assistance, reflecting the hotel’s commitment to guest satisfaction. As a platform and a company, Exabits aims to provide investors with equal opportunities to participate in this growing IT economy.
As demand for computing increases, so does the appetite for investment opportunities in this rapidly evolving space. With the continued growth of AI, 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 will continue to expand the access to this popular technology.
