
Meta’s new AI chips will begin production in September
Quick Answer
Meta is set to begin production of its new AI-specific chips in September, developed in collaboration with Broadcom and manufactured by TSMC.
Quick Take
Meta is set to begin production of its new AI-specific chips in September, developed in collaboration with Broadcom and manufactured by TSMC. These chips aim to reduce reliance on Nvidia and AMD GPUs, with plans for extensive deployment in AI workloads and model training.
Key Points
- Meta's AI chips are part of the MTIA program, with modular designs for evolving AI needs.
- The company expects to spend $125-$145 billion on AI-related capital expenditures this year.
- Meta plans to deploy 7 gigawatts of compute capacity in 2023, doubling in 2024.
- The new chips will support training for ranking and recommendation algorithms.
- Meta has secured deals with ARM, AMD, and Amazon for additional compute resources.
📖 Reader Mode
~3 min readIn a bid to lower its GPU costs amid an unprecedented component shortage, Meta is on track to start making the latest versions of its AI-specific chip in September, Reuters reported, citing an internal memo.
At least one chip sailed through its testing phase in about six weeks, the memo said. Meta is working with Broadcom on the chip design, but it will use Taiwan Semiconductor Manufacturing Company (TSMC) to manufacture them. It is also buying RAM from Samsung, storage from Sandisk, and fiber-optic equipment from Sumitomo Electric, according to the report.
Meta detailed the four new chips, developed under its Meta Training and Inference Accelerator (MTIA) program, in March, some of which are currently in deployment or will be this year or next. The company is taking a modular approach to designing these chips, anticipating that their needs will change as AI evolves rapidly by the time the chips are in production.
“Each MTIA generation builds on the last, using modular chiplets, incorporating the latest AI workload insights and hardware technologies, and deploying on a shorter cadence,” the company wrote at the time.
The chips are expected to help the company save on buying GPUs from chipmakers like Nvidia and AMD, although it still expects to spend plenty with those providers as well, Reuters reports. Meta intends to use the MTIA chips for training models for its ranking and recommendation algorithms, broader AI workloads, and inference aimed at its applications. The social media company has been producing its own AI chips since 2023.
Meta has been spending massively on securing enough compute capacity to power its various AI efforts. The company in April said it expects capital expenditures between $125 billion and $145 billion this year, a lot of which is going toward its AI efforts.
The company has been striking data center and power deals across the world, spending tens of billions to secure computing capacity to train and deploy its new Muse Spark series of AI models. It plans to deploy 7 gigawatts of compute this year, and double that next, according to Reuters, which cited the memo.
It also signed a deal with ARM last year to secure compute for its recommendation systems, in addition to a multibillion-dollar deal with AMD for its Instinct GPUs and a multibillion-dollar deal with Amazon to use the cloud giant’s homegrown CPUs for AI-related needs.
Meta isn’t the only company trying to stem the tide of capital going to Nvidia. OpenAI last month unveiled an inference processor that it is building with Broadcom, and Anthropic is said to be considering developing its own chips with Samsung. Amazon and Google both develop their own chips for AI training and inference, and there’s a host of startups building in the space to meet skyrocketing demand.
Meta declined to comment.
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Ram is a financial and tech reporter and editor. He covered North American and European M&A, equity, regulatory news and debt markets at Reuters and Acuris Global, and has also written about travel, tourism, entertainment and books.
You can contact or verify outreach from Ram by emailing ram.iyer@techcrunch.com.
— Originally published at techcrunch.com
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