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메타, AI 가속용 커스텀 칩 MTIA 4세대 2년 내 배포

JD 젤리비 편집국 · 2026.03.11 SHARE · COPY LINK

무슨 발표인가

  • MTIA(메타 학습·추론 가속기) 칩 개발 중
  • 2년 내 4세대 칩 개발·배포 예정
  • 추론 작업 전용으로 수십만 개 칩 운영 중

원문 (영어)

In 2023, we developed the Meta Training and Inference Accelerator (MTIA) , a family of custom-built silicon chips to power our AI workloads efficiently. Now, we’re developing and deploying four new generations of chips within the next two years — a much faster pace than typical chip cycles — to support ranking, recommendations, and GenAI 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 center of our AI infrastructure strategy.

A Custom Solution We deploy hundreds of thousands of MTIA chips for inference workloads across both organic content and ads on our apps. These chips are specifically designed for our workloads, and are part of a custom full-stack solution, helping us create a highly optimized system that’s tailored to our needs.

This system achieves greater compute efficiency than general use chips for our intended purposes, making MTIA much more cost efficient. Four Chips in Two Years We’re 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 modularity of our silicon allows these new chips to drop into existing rack system infrastructure, accelerating time-to-production.

원문: Meta Newsroom — "Expanding Meta’s Custom Silicon to Power Our AI Workloads" (2026-03-11) 공식 원문: https://about.fb.com/news/2026/03/expanding-metas-custom-silicon-to-power-our-ai-workloads/

#Meta Newsroom
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메타, AI 가속용 커스텀 칩 MTIA 4세대 2년 내 배포

젤리비 편집국·2026.03.11·4 MIN
IN THIS PIECE
무슨 발표인가 원문 (영어)

무슨 발표인가

  • MTIA(메타 학습·추론 가속기) 칩 개발 중
  • 2년 내 4세대 칩 개발·배포 예정
  • 추론 작업 전용으로 수십만 개 칩 운영 중

원문 (영어)

In 2023, we developed the Meta Training and Inference Accelerator (MTIA) , a family of custom-built silicon chips to power our AI workloads efficiently. Now, we’re developing and deploying four new generations of chips within the next two years — a much faster pace than typical chip cycles — to support ranking, recommendations, and GenAI 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 center of our AI infrastructure strategy.

A Custom Solution We deploy hundreds of thousands of MTIA chips for inference workloads across both organic content and ads on our apps. These chips are specifically designed for our workloads, and are part of a custom full-stack solution, helping us create a highly optimized system that’s tailored to our needs.

This system achieves greater compute efficiency than general use chips for our intended purposes, making MTIA much more cost efficient. Four Chips in Two Years We’re 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 modularity of our silicon allows these new chips to drop into existing rack system infrastructure, accelerating time-to-production.

원문: Meta Newsroom — "Expanding Meta’s Custom Silicon to Power Our AI Workloads" (2026-03-11) 공식 원문: https://about.fb.com/news/2026/03/expanding-metas-custom-silicon-to-power-our-ai-workloads/

#Meta Newsroom
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