Meta Platforms has revealed a roadmap for a new generation of in-house artificial intelligence chips as the company accelerates its investments in AI infrastructure. The chips are designed to power Meta’s growing network of data centers and support the increasing demand for AI-driven services across its platforms.

The new processors are part of Meta’s Meta Training and Inference Accelerator (MTIA) program. According to the company, four generations of custom AI chips will be deployed over the coming years to improve performance and efficiency for AI workloads.

The first chip in the roadmap, called MTIA 300, has already been deployed and is currently powering ranking and recommendation systems across Meta’s platforms, including Facebook and Instagram. Additional chips—MTIA 400, MTIA 450, and MTIA 500—are expected to roll out gradually through 2027.

Meta plans to release a new chip approximately every six months as part of its strategy to scale artificial intelligence capabilities. The chips are optimized to handle tasks such as AI model inference, recommendation engines, and large-scale data processing.

The move comes as global technology companies race to build specialized hardware for AI. By designing its own processors, Meta aims to reduce reliance on external chip suppliers such as Nvidia while improving efficiency and lowering long-term infrastructure costs.

Industry analysts say custom AI chips allow large tech companies to optimize hardware specifically for their software platforms. This approach can significantly improve performance for machine learning systems that power social media feeds, advertising systems, and AI assistants.

Meta has also announced plans to invest tens of billions of dollars in expanding its AI data center capacity over the next several years. The company believes that custom silicon will play a key role in supporting future generative AI systems and advanced machine learning models.

As competition intensifies in the global AI race, Meta’s chip strategy reflects a broader industry trend in which major technology companies are developing their own specialized hardware to gain greater control over AI infrastructure.