Meta has moved past PowerPoint chip designs and into actual manufacturing. According to an internal memo, production starts this month on Iris, its first proprietary AI accelerator in the new MTIA generation — another step in hyperscalers' race to stop depending exclusively on Nvidia.
Iris is the code name for Meta's new datacenter chip, part of its MTIA program (Meta Training and Inference Accelerators) — a four-generation family of in-house accelerators designed to train and run inference for the AI models behind Facebook and Instagram. Meta designed the chip together with Broadcom, while actual fabrication is handled by TSMC — the same "in-house design, third-party manufacturing" playbook already used by Google (TPU) and Amazon (Trainium).
According to the internal memo cited by Reuters, Iris completed its bug-testing phase in roughly six weeks with no significant issues, clearing the way to begin manufacturing in September 2026. Iris production sits within a broader Meta-Broadcom partnership, extended this year through 2029 to cover multiple future MTIA generations.
Meta isn't alone in this move. Google is several generations into TPU, Amazon has Trainium and Inferentia, and Microsoft is developing its own Maia silicon. The pattern is consistent: the more Big Tech spends on AI compute, the more economic sense it makes to design at least a portion of that hardware in-house, even if Nvidia remains the dominant supplier for the bulk of the workload.
Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.
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