AI Demand Leads to “GPU-as-a-Service” Industry
Increased interest in artificial intelligence creates a huge demand for computing strength. Throughout the world, companies are trying to keep up with a wide amount of graphics processing units needed to run more advanced artificial intelligence models. Although graphics processing units are not the only option to operate the artificial intelligence model, they have become preferred devices because of their ability to deal with multiple operations efficiently-an important feature when developing deep learning models.
But not every emerging company of artificial intelligence has capital to invest in huge numbers of graphics processing units now required to operate an advanced model. For some, it is a better deal to use external sources. This has led to the emergence of a new work: GPU-AS-ARVICE (GPUAAS). In recent years, companies such as excess, Kinesisand RunbodAnd Vast It has spread to their customers from the necessary treatment force.
While technology giants such as Amazon or Microsoft that provide cloud computing services have their infrastructure, smaller startups such as Kinesis have created technologies to achieve the best in the current inactivity account.
“Companies need an account. They need to train the form or run their applications; Benna Khimani, Kinesis, says, they do not necessarily need to own or manage servers.”
studies It has shown More than half of the current graphics processing units are not in use at any time. Whether we are talking about personal computers or massive server farms, much of the treatment capacity is not exploited. What Kinesis is doing is to determine lethargy account – both for graphics processing units (graphics processing units (central processing units) – in servers all over the world and assemble them in a single computing source for companies to use. Kinesis partners with universities, data centers, companies and individuals who are ready to sell their unused computing power. Through a special program installed on their servers, Kinesis discovers the processing units of processing Inactivity, prepares it, and provides it for its customers for temporary use.
“In Kinesis, we have developed a technique to collect together, the power of lethargy and re -display it to a computing platform without a servant that automatically runs,” says Khimani. Kinesis customers may be able to choose from the place where they want graphics processing units or central processing units.
Artificial intelligence grows faster than servers that can continue
GPUAS fills a growing gap in the industrial intelligence. Since learning models become more sophisticated, they need more strength and infrastructure that can process information faster and faster. In other words, without enough graphics processing units, large artificial intelligence models cannot mention improvement. In October, CEO of Openai, Sam Altman, I confess The company did not launch products as much as they wished that they were facing “a lot of restrictions” with its ability to compute.
Also in October, the financial manager of Microsoft, Amy Woods, He said The company’s investors at a phone conference requesting artificial intelligence “higher” than “their available energy”.
The largest advantage in Economic GPUAAS. By eliminating the need to buy and maintain material infrastructure, it allows companies to avoid having to invest in servers and information technology management, and instead put their resources towards improving deep learning, large language, and large models. It also allows customers to pay for the exact amount of the graphics processing units they use, and to provide inevitable lethargy costs that will come with their own servers.
Strain companies without a server, such as Kinesis, also claim that they are friendly to the environment from traditional companies that represent cloud computers. By taking advantage of the unused current processing units instead of running additional servers, the company says it significantly reduces energy consumption. In the past five years, major technology companies such as Google and Microsoft have seen their carbon emissions rising due to the amount of energy consumed by artificial intelligence. In response, some have turned into nuclear energy to operate their servers sustainable. Kinesis and other new startups provide a third road that does not need to be connected to servers.
“Industrial leaders are strongly committed to sustainability,” says Khimani. “Focusing on innovation and efficiency, they can improve the current computing power that is already active energy and consumption, instead of adding more continuous servers for each new application they operate.”
The increasing demand for machine learning and the consumption of massive data turns GPUAAS into a very profitable technical sector. In 2023, the market size in industry It was valuable At 3.23 billion US dollars; In 2024, it grew to $ 4.31 billion. It is expected to rise to $ 49.8 billion by 2032.
“The artificial intelligence industry is rapidly progressing to a stage where the focus turns from just building models and training to improving efficiency,” says Khimani. “Customers are increasingly raising questions like,” when training a new model, how can we do it very targeted and do not consume a periphery of data that requires a huge amount of arithmetic and energy? “
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2025-01-20 15:00:00
