Meta Starts Testing its Maiden Internal AI Chip

Meta is trying out its first chip created for training AI models, marking a key step toward reducing its dependence on third-party chipmakers. According to sources, the social media behemoth has begun a limited rollout of the chip and may increase production if testing proves successful.

Developing custom chips is part of the company’s broader strategy to reduce its infrastructure costs as it heavily invests in AI-powered tools. The company, which owns WhatsApp, Facebook, and Instagram, has projected its 2025 expenses to be approximately $119 billion, with up to $65 billion going toward AI infrastructure.

Meta’s new chip is a specialized accelerator optimized for AI roles, making it more energy-efficient than conventional GPUs commonly used in AI applications. The company has partnered with TSMC, a Taiwan-based semiconductor manufacturer.

The trial phase commenced after Meta finished its first “tape-out,” a key step in chip development where an early design is sent to a factory for production. This stage, which typically takes 3 to 6 months and costs millions, does not guarantee success. Meta would have to go through the procedure again if problems occurred.

The new chip is a component of the company’s MTIA series, a project that has faced hurdles over the years. Meta previously abandoned a chip at a comparable development stage. But last year, the business was able to boost content suggestions on Instagram and Facebook by effectively implementing an MTIA chip for inference—the process of running AI models in real time.

Meta aims to transition AI training processes to internal chips. The chips will support recommendation models before being adapted for generative AI apps, such as Meta’s AI chatbot.

Previously, the company scrapped a custom inference chip after it underperformed in small-scale testing and instead purchased billions of dollars’ worth of GPUs from Nvidia in 2022. Meta remains one of Nvidia’s largest clients, using GPUs to power AI systems for ad targeting and recommendation algorithms.

However, the growing reliance on scaling up LLMs with more computing power and data is facing scrutiny. Some researchers question whether this approach will continue yielding major AI advancements.

The emergence of more efficient models, such as those from Chinese DeepSeek, has fueled this skepticism. In early 2025, DeepSeek’s release of low-cost AI models led to a temporary drop in Nvidia’s stock value, though the company’s shares later rebounded as investors remained confident in its industry dominance.

As more firms, such as D-Wave Quantum Inc. (NYSE: QBTS), continue to unveil new tech innovations, the U.S. is likely to solidify its position as the leader in the AI industry.

NOTE TO INVESTORS: The latest news and updates relating to D-Wave Quantum Inc. (NYSE: QBTS) are available in the company’s newsroom at https://ibn.fm/QBTS

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