Cerebras Systems raises $5.5B in blockbuster IPO after nearly burning out with $8M monthly spend

Reviewed byNidhi Govil

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Cerebras Systems raised $5.5 billion in its IPO, valuing the AI chipmaker at roughly $60 billion. But the company's success story nearly ended in 2019 when it was burning $8 million monthly trying to solve a technical problem that had stumped the semiconductor industry for decades. The breakthrough came in July 2019 when the team finally cracked the packaging challenge for its dinner plate-sized chip.

Cerebras Systems Achieves $60 Billion Valuation After Blockbuster IPO

Cerebras Systems raised $5.5 billion in its Cerebras IPO on Thursday, pricing shares at $185—far above its initial range of $115 to $125 and even beyond the revised $150-$160 target

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. The AI chipmaker ended its first week of trading worth approximately $60 billion, with co-founder CEO Andrew Feldman's stake valued at nearly $1.9 billion and co-founder CTO Sean Lie's at about $1 billion

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. The company closed its first day trading on Wall Street with a market cap just below $100 billion, placing it among tech's biggest-ever IPOs alongside companies like Meta and Alibaba

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Source: FT

Source: FT

The milestone represents a dramatic turnaround for the AI chip company, which first filed to go public in 2024 but shelved those plans amid concerns about a large investment from Abu Dhabi-based Group 42 and an endless review from CFIUS

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. IPO ambitions resurfaced in April when Cerebras reported revenues of $510 million in 2025, up 76 percent year-over-year, and a massive swing to $237.8 million in net income compared to losing nearly half a billion the year before

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The Near-Death Experience That Almost Killed the AI Hardware Industry Disruptor

Today's success masks a harrowing near-failure that nearly destroyed Cerebras Systems in 2019. When the company was three years old, it was burning $8 million a month trying to solve one technical problem that no one in the semiconductor industry thought could be done

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. "At this point, we had incinerated nearly $200 million trying to solve one technical problem," Feldman told TechCrunch

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Every few weeks, Feldman made the painful walk to board meetings to report another failure and more money burned. The problem centered on "packaging"—everything after manufacturing the silicon itself, including adhering it to a motherboard, delivering power, managing heating and cooling, and creating the pipes that would deliver and return data

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. Cerebras' chips were 58 times larger than typical GPUs and used 40 times as much power as anybody had ever used

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. There were no premade heat sinks, no vendors, and no manufacturing partners for such a large-scale AI chip.

The team destroyed an enormous number of chips through trial and error. In one instance, they had to invent their own machine that could bolt-in 40 screws simultaneously to secure the wafer to a board without cracking it

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. The breakthrough came in July 2019 when the entire founding team stood in the lab watching lights flash on the computer, stunned they'd solved the problem. "That was one of the greatest moments of my life," Feldman said

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Revolutionary Wafer-Scale Engine Architecture Challenges Nvidia Competitor Status

Source: The Register

Source: The Register

Founded in 2015 by former SeaMicro head Andrew Feldman, Cerebras Systems took a radically different approach to chip design

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. Rather than cutting up a wafer into smaller chips just to reconnect them again, Cerebras created the Wafer-Scale Engine (WSE), a giant AI chip measuring 46,225 square mm—about the size of dinner plate-sized AI accelerators

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The microprocessor industry had spent over 50 years making CPUs faster by cramming more transistors onto silicon wafers and dicing them into tinier pieces. But AI required so much AI computing power that many chips had to be strung together and forced to communicate

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. Cerebras' founders believed turning a whole wafer into one giant, powerful chip would work faster.

The first-generation WSE featured a novel compute engine designed to speed up highly sparse matrix multiply-accumulate operations common in deep learning, boosting effective computational output from 2.65 16-bit petaFLOPS to 26.5

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. By the third iteration, the WSE-3 boasted massive memory bandwidth of 21 PB/s—nearly 1000x faster than Nvidia's new Rubin GPUs

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AI Inference Capabilities Drive Customer Partnerships and Market Position

While Cerebras initially focused on AI training, the company announced an inference-as-a-service offering in mid-2024 to rival competing chip startups like Groq and SambaNova

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. The massive SRAM capacity and memory bandwidth made Cerebras AI accelerators particularly well suited to high-speed large language models inference

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. According to Artificial Analysis, Cerebras' kit can churn out more than 2,200 tokens per second when running GPT-OSS 120B High, 2.8x faster than the next closest GPU cloud Fireworks

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Cerebras now counts OpenAI, Amazon Web Services, G42, and Saudi's Mohamed bin Zayed University of Artificial Intelligence as customers

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. OpenAI recently endorsed the technology in a $20 billion deal that will eventually deploy 750 megawatts of Cerebras' chips and involve co-designing hardware

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. OpenAI also extended Cerebras a $1 billion working capital loan secured by warrants that conditionally grant OpenAI about 33 million shares—worth over $9 billion at Friday's closing price of $279

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Source: Silicon Republic

Source: Silicon Republic

As part of the loan deal, Cerebras agreed not to sell to specific OpenAI competitors, though Feldman said this restriction is temporary and "designed to make sure that we could get OpenAI the capacity"

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. The company has also struck partnerships with Meta, Mistral, Cognition, and Windsurf

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Market Timing Captures Surge in Demand for AI Computing Infrastructure

The Cerebras IPO marked an early test of Wall Street's appetite for new AI listings ahead of hotly anticipated debuts from OpenAI and Anthropic, which could follow Elon Musk's SpaceX into public markets as soon as this year

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. The valuation represents a significant jump from a private financing in September that priced the Silicon Valley company at $8.1 billion

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Like other chip providers, Cerebras benefits from an industry-wide shortage of computing power that Big Tech companies and AI startups need

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. That computing crunch has intensified this year with the arrival of agentic AI tools like Claude Code, which require even more processing power for AI inference involved in responding to queries

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. "We've now arrived at the era of agentic AI, where inference is key," noted industry observers

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Cerebras argues its large size is well suited to inference because it eliminates the need to link many smaller chips together, which can slow responses to users' AI queries

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. The company faces challenges common to semiconductor companies, including securing manufacturing space at Taiwan Semiconductor Manufacturing Company when big companies have snapped up much capacity

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. Very large chips pose particular manufacturing challenges because they're more likely to contain flaws, though Cerebras says its GPU architecture can accommodate these flaws, boosting production yields and margins

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