Meta has unveiled four new custom chips designed for artificial intelligence workloads as part of its expanding data center infrastructure. The processors are part of the company’s Meta Training and Inference Accelerator (MTIA) family, a line of chips built to handle specific AI tasks within Meta’s platforms.

The announcement marks the latest step in Meta’s push to reduce reliance on external chip vendors by designing its own silicon optimized for internal workloads. According to Meta Vice President of Engineering Yee Jiun Song, custom chips allow the company to improve price-performance efficiency across its data centers while diversifying its hardware supply chain.

“This also provides us with more diversity in terms of silicon supply and insulates us from price changes to some extent,” Song said.

Meta first revealed the MTIA architecture in 2023 and released a second-generation version in 2024. The new lineup significantly expands the platform as the company scales its AI infrastructure to support recommendation systems and generative AI features across its products.

Four New Chips Target Different AI Workloads

The first of the new chips, MTIA 300, has already been deployed in Meta data centers. It is designed to train smaller AI models used for core platform tasks such as ranking and recommendation algorithms. These systems determine which posts, ads, and videos appear in users’ feeds across services including Facebook and Instagram.

Three additional chips are currently in development. The MTIA 400 is nearing deployment after completing testing, while the MTIA 450 and MTIA 500 are scheduled to become operational by 2027.

Unlike the MTIA 300, the upcoming chips will focus on generative AI inference workloads such as generating images and videos from text prompts. However, Meta said the processors will not be used to train large language models.

Song noted that Meta plans to release new chips roughly every six months as the company rapidly expands computing capacity. Each chip generation is expected to remain in service for more than five years.

Data Center Expansion and AI Infrastructure

The new processors will support Meta’s massive data center expansion. The company is currently building a large facility in Louisiana and additional centers in Ohio and Indiana. Reports also indicate that Meta is exploring leasing space at a major AI data center site in Texas.

The custom chips are manufactured by Taiwan Semiconductor Manufacturing Company, though Meta did not confirm whether production will occur at the company’s new fabrication facilities in Arizona.

Meta’s in-house silicon strategy follows a broader industry trend among major technology companies developing application-specific integrated circuits, or ASICs, for AI workloads. These chips are typically smaller and more energy-efficient than general-purpose GPUs but are optimized for narrower tasks.

Google pioneered this approach with its Tensor Processing Units in 2015, and Amazon followed with custom chips for its cloud services in 2018. Unlike those companies, Meta uses its MTIA chips exclusively for internal operations rather than offering them through a public cloud platform.

Despite building its own silicon, Meta continues to rely heavily on external hardware suppliers. The company recently signed agreements to deploy millions of Nvidia GPUs and up to six gigawatts of AMD GPUs across its data centers.

Song acknowledged that securing high-bandwidth memory remains a potential constraint as AI infrastructure spending increases across the technology industry. However, he said Meta has diversified its supply chain and believes it has secured the resources required for its planned deployments.

Anthropic has announced the launch of the Anthropic Institute, a research initiative focused on examining the societal, economic, and governance challenges posed by rapidly advancing AI systems.

The institute will draw on internal research across Anthropic to provide insights for policymakers, researchers, and the public as AI systems grow more capable. The company said the effort aims to improve understanding of how advanced AI could reshape economies, jobs, legal systems, and governance structures.

The initiative will be led by Anthropic co-founder Jack Clark, who will assume a new role as the company’s Head of Public Benefit. The institute’s interdisciplinary team will include machine learning engineers, economists, and social scientists working together to analyze the broader implications of frontier AI technologies.

Anthropic said the pace of AI progress has accelerated rapidly in recent years. The company took two years to release its first commercial model and only three more years to develop systems capable of discovering cybersecurity vulnerabilities, performing complex professional tasks, and contributing to AI research itself.

The institute will study several key questions related to advanced AI development, including how the technology may transform labor markets, influence economic growth, and affect societal resilience. It will also examine governance challenges such as how AI systems should be regulated and how organizations should manage the values embedded in advanced models.

Research Focus and Policy Engagement

The Anthropic Institute will integrate and expand three existing research groups inside the company. These include the Frontier Red Team, which tests the limits and risks of AI systems, the Societal Impacts team studying real-world AI adoption, and the Economic Research team analyzing labor and macroeconomic effects.

In addition to continuing existing research programs, the institute plans to explore new areas including forecasting future AI progress and studying how powerful AI systems could interact with legal systems and regulatory frameworks.

Anthropic has also announced several key hires for the institute. Matt Botvinick, previously a senior research leader at Google DeepMind and a resident fellow at Yale Law School, will lead research on AI and the rule of law. Economist Anton Korinek from the University of Virginia will help study how advanced AI could reshape economic activity. Zoë Hitzig, who previously researched AI’s economic impacts at OpenAI, will contribute to connecting economic research with model development.

Alongside the institute launch, Anthropic is expanding its public policy organization. The company plans to open its first Washington, D.C. office this spring as it increases engagement with policymakers on issues including AI safety, infrastructure investment, and export controls.

The company said the institute will publish research and engage with external stakeholders to help societies prepare for the potential benefits and risks of transformative AI technologies as development accelerates.

Nvidia and Thinking Machines Lab have announced a multiyear strategic partnership focused on deploying large-scale AI infrastructure to support next-generation model development. The collaboration will deploy at least one gigawatt of Nvidia’s upcoming Vera Rubin systems to power Thinking Machines’ frontier AI training and model platforms.

The deployment is expected to begin early next year and will provide the computing capacity needed to train advanced AI models at scale. The initiative also includes joint efforts to design optimized training and inference systems built specifically for Nvidia architectures.

Through the partnership, the companies aim to broaden access to high-performance AI infrastructure and models for enterprises, research institutions, and the scientific community. The companies said the project is intended to support the development of customizable AI systems that organizations can adapt to their specific needs.

Nvidia has also made a significant financial investment in Thinking Machines Lab as part of the agreement, though financial terms were not disclosed.

Scaling Compute for Frontier AI Research

The collaboration reflects growing demand for massive computing resources as organizations train increasingly sophisticated AI models. Frontier models require large clusters of specialized hardware capable of handling enormous datasets and complex training workflows.

Nvidia’s Vera Rubin architecture represents the company’s next generation of AI computing platforms, designed to deliver significantly greater performance and efficiency for large-scale AI workloads. The systems are expected to power both training and inference tasks for advanced machine learning models.

“AI is the most powerful knowledge discovery instrument in human history,” said Nvidia founder and chief executive Jensen Huang. “Thinking Machines has brought together a world-class team to advance the frontier of AI. We are thrilled to partner with Thinking Machines to realize their exciting vision for the future of AI.”

Thinking Machines Lab cofounder and chief executive Mira Murati said the partnership will accelerate the company’s efforts to develop more flexible AI systems.

“Nvidia’s technology is the foundation on which the entire field is built,” Murati said. “This partnership accelerates our capacity to build AI that people can shape and make their own, as it shapes human potential in turn.”

The companies said the collaboration is designed to advance research and infrastructure needed to develop AI systems that are more understandable, customizable, and collaborative. By combining high-performance computing with new AI model architectures, the partnership aims to expand access to advanced AI capabilities across scientific research and enterprise applications.

The partnership also comes as the startup navigates internal changes. Thinking Machines Lab recently lost two founding members to Meta, underscoring ongoing competition for top AI talent even as the company continues expanding its infrastructure and research ambitions.