AI is running out of computing power. Here is the answer

The hype suggests that artificial intelligence (AI) is already everywhere, but in reality, the technology that drives it is still developing. As AI becomes more complex and versatile, it becomes increasingly difficult for the hardware to keep up if there are no parallel improvements. Creating just one language model for ChatGPT comes with a staggering price tag of 70,000,000 USD.

“We’re running out of computing power. AI models are growing exponentially, but the hardware to train these behemoths and run them on servers in the cloud hasn’t advanced as quickly,” said IBM. As AI models continue to grow in size, will hardware keep pace?

AI’s demand for computing power

AI will empower all industries. It is estimated that, by 2025, AI will be a market worth US$208 billion and will be in full play in many fields such as autonomous driving, smart finance, smart healthcare, smart retail, and entertainment. As the computing power of chips used in AI-powered smart scenarios goes up and AI penetrates more industries, both demand for computing power and demand for cloud computing centers, edge devices, and terminal NPUs will rocket. By 2030, the demand for computing power in AI-powered fields will reach 16,000 EFLOPS, equivalent to the combined computing power provided by 160 billion Qualcomm Snapdragon 855 chips with built-in artificial intelligence.

Challenges with computing power

Despite advancements in technology, the cost and efficiency of computing power remain significant obstacles to the growth of AI. As modern AI algorithms demand high levels of power, access to this technology remains limited for many due to financial constraints. Additionally, the industry has faced criticism for its impact on the environment, particularly in relation to its carbon footprint. To address these challenges, it is crucial that efforts are made to optimize the use of available computing power, both in terms of cost and environmental sustainability.

Computing power and AI

A whitepaper by IDC indicates that economic growth is directly related to the development of computing power. Each point of growth in the computing index leads to an increase of 3.3% in the digital economy and 1.8% in GDP. Emerging technologies and computing are mutually beneficial, so a rise in computing power drives a rise in AI, which in turn leads to a rise in computing power. Advancements in computing power are closely linked with productivity and are a crucial driving force behind the growth of artificial intelligence.

Exabits: the answer to computing power needs

As companies look for ways to cut costs and optimize their computing power, a new solution has emerged: utilizing the shared computing power of a group of users through a web3 model. A distributed network of individuals contributes computational power for the training of the algorithm. This approach not only reduces the cost of investing in AI hardware or paying for expensive cloud computing services but also provides an incentive for users to share their resources by allowing them to earn money.

Exabits is a platform that facilitates just that. It is a decentralized, community-driven platform that includes AI/ML companies and individuals who utilize the collective resources of our distributed GPU system. By exchanging computing power with other members, Exabits has the potential to reduce computational costs by threefold compared to traditional cloud computing services. The ultimate goal is to make AI solutions more widely accessible, economical, and user-friendly for a variety of customers.

While cloud services such as AWS, Google Cloud, and Microsoft Azure can provide the necessary computing power, Exabits offers a more cost-effective alternative. This could potentially revolutionize the field of AI, bringing us closer to the singularity and ushering in a new era for human civilization.

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