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. As such, this year saw an overwhelming customer demand for AI products and even greater pressure on data centers to deliver AI infrastructure to boost efficiency. 

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. But before organizations fully embrace the exciting potential of this hardware, we need to carefully consider the complexities and challenges that come with them.

It’s true that 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 these technologies are here to stay. 

The growth has just started 

Unsurprisingly, 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—a management consulting 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. If we’re assessing some of the barriers to investing in this software, one of the biggest hurdles investors face is the initial cost.

The costs of advanced GPUs like NVIDIA’s A100 or H100 can be upwards of 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 corporations.  

Beyond the hefty price tag, the hardware itself isn’t for the faint of heart. It requires a thorough understanding of optimizing and managing these resources effectively. Investors should have specialized knowledge in the hardware and software, making technical expertise a prerequisite. 

Even if affordability and technical challenges weren’t barriers to investing, a significant obstacle remains: Supply or lack thereof. The Bain & Company report reveals that demand for AI components could grow by 30 percent or more, outpacing supply capabilities. 

While investing in compute may seem out of reach, there are new models 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, Exabits offers users an opportunity to become stakeholders in the AI compute economy, allowing them to earn rewards and revenue without needing to manage the complexities of hardware ownership. 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 investing in AI compute more accessible. 

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 world 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 this 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. 

As part of a new agreement, Phala Network will enhance its AI capabilities using io.net’s GPU cloud infrastructure, making advanced computation more accessible.

GPU cloud network io.net (IO) has partnered with Phala Network to improve secure computation and decentralized AI capabilities, according to a press release shared with crypto.news. 

The IO Cloud service provided by io.net allows users to access high-performance GPUs on demand, reducing costs compared to traditional cloud services by up to 90%. Machine learning engineers and developers can access the computational power needed to train and deploy AI models without investing in expensive hardware.

The collaboration will allow Phala Network to tap into io.net’s cloud network, IO Cloud, for powerful GPU hardware, expanding the reach of Phala’s decentralized AI ecosystem.

This partnership strengthens Phala’s ability to run complex AI applications securely by leveraging Nvidia H100 and H200 GPUs, known for their advanced cryptographic protections. In other words, this collaboration will make it easier and more cost-effective for developers to run complex AI tasks while ensuring data security.

Phala Network will use io.net’s GPU resources to support its Trusted Execution Environment CPU nodes. TEEs are isolated sections of a computer’s hardware designed to securely handle sensitive data.

AI workloads 

According to the press release, Phala introduced the first benchmark for TEE-enabled GPUs in August. Through io.net’s network, Phala ensures that AI workloads are processed securely with Nvidia’s confidential computing features, such as encrypted memory and secure boot.

Phala Network is known for offering a decentralized platform where developers can execute complex tasks outside of traditional blockchain networks while maintaining privacy. Its infrastructure of over 40,000 TEE CPU nodes enables Web3 applications to handle computationally intensive tasks while safeguarding data privacy.

Io.net and Phala Network will conduct research and benchmarking, starting with Nvidia’s H100 and H200 GPUs. 

They plan to integrate Phala’s AI agents and hardware into the IO Network, enhancing both parties’ ability to support secure and decentralized AI operations, according to the release. This could lead to more accessible and efficient AI-driven Web3 applications.