Facebook owner Meta Platforms Inc is testing its first in-house chip for training artificial intelligence (AI) systems, a key milestone as it moves to design more of its own custom silicon and reduce reliance on external suppliers like Nvidia Corp, two sources told Reuters.
The world’s biggest social media company has begun a small deployment of the chip and plans to ramp up production for wide-scale use if the test goes well, the sources said.
The push to develop in-house chips is part of a long-term plan at Meta to bring down its mammoth infrastructure costs, as the company places expensive bets on AI tools to drive growth.
Photo: Reuters
Meta, which also owns Instagram and WhatsApp, has forecast total expenses of US$114 billion to US$119 billion for this year, including up to US$65 billion in capital expenditure largely driven by spending on AI infrastructure.
One of the sources said Meta’s new training chip is a dedicated accelerator, meaning it is designed to handle only AI-specific tasks. This can make it more power-efficient than the integrated graphics processing units (GPUs) generally used for AI workloads.
Meta is working with Taiwan Semiconductor Manufacturing Co (TSMC, 台積電) to produce the chip, this person said.
The test deployment began after Meta finished its first “tape-out” of the chip, a significant marker of success in silicon development work that involves sending an initial design through a chip factory, the other source said.
A typical tape-out costs tens of millions of US dollars and takes roughly three to six months to complete, with no guarantee the test will succeed. A failure would require Meta to diagnose the problem and repeat the tape-out step.
Meta and TSMC declined to comment.
Meta executives have said they want to start using their own chips by next year for training, or the compute-intensive process of feeding the AI system reams of data to “teach” it how to perform.
As with the inference chip, the goal for the training chip is to start with recommendation systems and later use it for generative AI products like chatbot Meta AI, the executives said.
“We’re working on how would we do training for recommender systems and then eventually how do we think about training and inference for gen AI,” Meta chief product officer Chris Cox said at the Morgan Stanley technology, media and telecom conference last week.
Cox described Meta’s chip development efforts as “kind of a walk, crawl, run situation” so far, but said executives considered the first-generation inference chip for recommendations to be a “big success.”
Meta previously pulled the plug on an in-house custom inference chip after it flopped in a small-scale test deployment similar to the one it is doing now for the training chip, instead reversing course and placing orders for billions of dollars worth of Nvidia GPUs in 2022.
The social media company has remained one of Nvidia’s biggest customers since then, amassing an arsenal of GPUs to train its models, including for recommendations and ads systems and its Llama foundation model series. The units also perform inference for the more than 3 billion people who use its apps each day.
The value of those GPUs has been thrown into question this year, as AI researchers increasingly express doubts about how much more progress can be made by continuing to “scale up” large language models by adding ever more data and computing power.
Those doubts were reinforced with the late-January launch of new low-cost models from Chinese start-up DeepSeek (深度求索), which optimize computational efficiency by relying more heavily on inference than most incumbent models.
In a DeepSeek-induced global rout in AI stocks, Nvidia shares lost as much as a fifth of their value at one point. They subsequently regained most of that ground, with investors wagering the company’s chips will remain the industry standard for training and inference, although they have dropped again on broader trade concerns.
ENERGY ISSUES: The TSIA urged the government to increase natural gas and helium reserves to reduce the impact of the Middle East war on semiconductor supply stability Chip testing and packaging service provider ASE Technology Holding Co (日月光投控) yesterday said it planned to invest more than NT$100 billion (US$3.15 billion) in building a new advanced chip testing facility in Kaohsiung to keep up with customer demand driven by the artificial intelligence (AI) boom. That would be included in the company’s capital expenditure budget next year, ASE said. There is also room to raise this year’s capital spending budget from a record-high US$7 billion estimated three months ago, it added. ASE would have six factories under construction this year, another record-breaking number, ASE chief operating officer Tien Wu
The EU and US are nearing an agreement to coordinate on producing and securing critical minerals, part of a push to break reliance on Chinese supplies. The potential deal would create incentives, such as minimum prices, that could advantage non-Chinese suppliers, according to a draft of an “action plan” seen by Bloomberg. The EU and US would also cooperate on standards, investments and joint projects, as well as coordinate on any supply disruptions by countries like China. The two sides are additionally seeking other “like-minded partners” to join a multicountry accord to help create these new critical mineral supply chains, which feed into
For weeks now, the global tech industry has been waiting for a major artificial intelligence (AI) launch from DeepSeek (深度求索), seen as a benchmark for China’s progress in the fast-moving field. More than a year has passed since the start-up put Chinese AI on the map in early last year with a low-cost chatbot that performed at a similar level to US rivals. However, despite reports and rumors about its imminent release, DeepSeek’s next-generation “V4” model is nowhere in sight. Speculation is also swirling over the geopolitical implications of which computer chips were chosen to train and power the new
Intel Corp is joining Elon Musk’s long-shot effort to develop semiconductors for Tesla Inc, Space Exploration Technologies Corp and xAI, marking a surprising twist in the chipmaker’s comeback bid. Intel would help the Terafab project “refactor” the technology in a chip factory, the company said on Tuesday in a post on X, Musk’s social media platform. That is a stage in the development process that typically helps make chips more powerful or reliable. The chipmaker’s shares jumped 4.2 percent to US$52.91 in New York trading on Tuesday. The Terafab project is a grand plan by Musk to eventually manufacture his own chips for
RSS