American startup is valued at nearly 100 billion USD thanks to ‘disk-sized’ AI chip

Cerebras, a business considered to be Nvidia’s rival, attracts attention with its disk-sized AI chip model.

Cerebras Systems, a startup specializing in AI chips, on May 14 conducted a highly successful IPO with an offering price of 185 USD per share. In the first trading session, the company’s stock value increased by 68%, bringing the company’s capitalization to nearly 100 billion USD.

Theo CNBC, The result not only puts Cerebras in the group of technology companies with the largest IPOs in history, but also shows the growing demand for AI chips as corporations look for solutions to replace Nvidia’s expensive and always in short supply GPU models.

 

Comparison of Cerebras WSE-3 chip size (left) and Nvidia B200. Image: Brains

Cerebras does not follow the trend of small-sized chips like Nvidia, but focuses on developing products the size of dinner plates. “We build the largest chips in the semiconductor industry. They are capable of processing more data in less time, delivering results much faster,” said Cerebras co-founder and CEO Andrew Feldman.

Nvidia currently holds a dominant position in the AI ​​chip industry thanks to its GPUs that are capable of multitasking, capable of operating many parallel calculations necessary for training large language models. However, the technology industry is shifting towards the era of AI agents, in which inference plays a key role. Training helps the AI ​​model learn patterns in large amounts of data, while inference uses AI to make decisions based on new information.

The inference process can run on low-performance chips programmed for specific tasks, such as Cerebras’ WSE-3. They are in the group of “task-specific integrated circuits” (ASIC), which is an increasingly competitive field as a series of corporations such as Google, Amazon, Meta and Microsoft are developing their own ASIC lines.

Cerebras claims the WSE-3 is 57 times larger than the largest GPU in the world today, and has 50 times more transistors.

The world’s most modern AI chips are being produced on TSMC’s 2 nm process, while the Cerebras chip is also manufactured by TSMC but uses an older 5 nm process.

Cerebras was founded in Silicon Valley in 2016, first filed for an IPO in 2024 but withdrew its decision due to scrutiny surrounding its dependence on its sole customer, G42, an AI company headquartered in the UAE and backed by Microsoft.

 

Cerebras WSE-3 chip. Image: Brains

Cerebras has sought to sell chips to businesses for years, but is now focusing on operating chips in its own data centers and serving as a cloud computing service, competing directly with Google, Microsoft, Oracle and CoreWeave.

Cerebras and OpenAI in January signed a cloud agreement worth $20 billion and valid until 2028. Amazon Web Services in March also announced that it is using Cerebras chips in its data centers.

“With inference products, demand is so great that our top challenge is securing supply. We are adding as much manufacturing and data center capacity as we can, but have sold out of 2027 supply,” said Chief Financial Officer Bob Komin.

Leading technology corporations are creating their own ASICs, while Cerebras competes with businesses that specialize in making chips for customers. Their main competitor is Groq, the company that signed a $20 billion contract with Nvidia in December 2025 to produce language processors (GLPU) on demand.

SambaNova and D-Matrix are also two formidable opponents of Cerebras in the AI ​​chip race. SambaNova is supplying SN50 chips to many corporations, including Hugging Face and Meta. Intel also participated in a $350 million funding round for this company in February, while CEI Intel Lip-Bu Tan has been President of SambaNova since 2017.

Cerabras’ IPO also paves the way for many ASIC chip manufacturing startups, such as Rebellions, to open public sales. This company in March received 400 million USD from many investors, including Samsung, after being valued at 2.34 billion USD and preparing for IPO.

By Editor

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