Meta plans to develop and deploy four new generations of chips within the next two years to support ranking, recommendations and GenAI workloads.
The new chips build on the company’s Meta Training and Inference Accelerator (MTIA), a family of custom-built silicon chips to efficiently power its AI workloads.
“As our current AI workloads continue to grow and evolve, we’re taking a portfolio approach to scale our infrastructure capacity by sourcing silicon from a range of industry leaders, while keeping our own MTIA custom silicon at the centre of our AI infrastructure strategy,” Meta says in a statement.
Meta deploys hundreds of thousands of MTIA chips for inference workloads across both organic content and ads on our apps. These chips are specifically designed for the workloads, and are part of a custom full-stack solution.
It is continuing to advance the MTIA roadmap by developing four new generations of chips, each bringing significant improvements in compute, memory bandwidth and efficiency.
MTIA 300 will be used for ranking and recommendations training, and is already in production. MTIA 400, 450 and 500 will be capable of handling all workloads, but we will primarily use these chips to support GenAI inference production in the near future and into 2027.
The MTIA strategy prioritises rapid, iterative development, an inference-first focus, and frictionless adoption by building natively on industry standards.
MTIA is built on industry‑standard software and hardware ecosystems, like PyTorch, vLLM, Triton and the Open Compute Project (OCP), enabling frictionless adoption of MTIA chips.
Beyond industry-standard software, MTIA’s system and rack solutions align with OCP standards, enabling MTIA to be seamlessly deployed in data centres.