The Meta Training and Inference Accelerator (MTIA) is a family of custom AI chips developed by Meta in partnership with Broadcom and manufactured by TSMC. Originally designed for ranking and recommendation workloads, MTIA has rapidly evolved into a multi-generational inference platform serving billions of users across Meta's platforms. ¹⁾

Meta's AI workload landscape spans four major domains: training vs inference, crossed with recommendation models vs generative AI. At planetary scale (3+ billion daily users), general-purpose GPUs proved economically unsustainable for inference workloads. MTIA was created to:

• Reduce total cost of ownership — MTIA 2i achieves 44% TCO reduction compared to GPUs ²⁾
• Mitigate supply risks from GPU vendor dependence
• Co-design hardware with Meta's specific model architectures

Meta has committed to one of the fastest custom chip iteration cycles in the industry — four generations in under two years:

All chips are built on RISC-V architecture and manufactured by TSMC with design partnership from Broadcom. ³⁾

A key architectural differentiator is MTIA's memory hierarchy: instead of costly HBM alone, MTIA uses large SRAM alongside LPDDR, optimizing for inference workloads where memory access patterns differ from training. This model-chip co-design approach allows Meta to tailor the hardware to its specific neural network architectures. ⁴⁾

• MTIA 2i (predecessor to MTIA 300 naming) is deployed at scale, serving billions of users for ranking and recommendation
• Hundreds of thousands of MTIA chips are already in production across Meta data centers
• MTIA handles the highest-volume AI workload by query count: deciding what content appears in Instagram and Facebook feeds ⁵⁾

Meta's MTIA program is part of a broader hyperscaler trend of building custom inference silicon to reduce dependence on NVIDIA GPUs. Similar efforts include Google's TPU, Amazon's Trainium/Inferentia, and Microsoft's Maia. Meta's approach is distinguished by its aggressive iteration speed and RISC-V architecture choice. ⁶⁾

• Neural Processing Unit (NPU)
• SRAM-Centric Chips
• Nvidia Vera Rubin