Meta is looking to spend up to $119 billion on AI in 2025, with about $65 billion allocated to AI infrastructure. As it has spent billions on Nvidia GPUs, Meta is now reducing its reliance on external suppliers and trying to optimize its own systems for jobs such as recommendation algorithms and generative AI.

Facebook owner Meta is testing its first in-house chip for training artificial intelligence systems, a key milestone as it moves to design more of its own custom silicon and reduce reliance on external suppliers like Nvidia ,

Meta is reportedly testing an in-house chip for training AI systems, a part of a strategy to reduce its reliance on hardware makers like Nvidia.

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 evaluating its AI training accelerator, which could reduce its reliance on Nvidia's AI GPUs for training.

The custom chip is designed to train Meta’s artificial intelligence systems and reduce the company’s dependency on third-party suppliers like Nvidia, with Meta aiming to use its own in-house chips by 2026.

Any setbacks in new chips meant that companies were forced to use older chips, which might not sit well with customers. Perhaps the AI industry is learning from this, because, according to a report from Reuters,

Meta has long been one of NVIDIA’s biggest customers, relying on its GPUs to train AI models. But, as the company has bet big on AI, its spending on AI infrastructure has soared through the roof. Now,