Uploaded November 2025 | Updated September 2026, 2 weeks ago
At Ray Summit 2025, Hongpeng Guo, Shang-Wen Li, Ramya Raghavendra, and Dong Wang from Meta share how the Fundamental AI Research (FAIR) team built Matrix, a powerful framework that lowers the barrier for researchers to conduct large-scale, data-centric experimentation with cutting-edge LLMs and multimodal models.
They begin by highlighting a key challenge in today’s AI landscape: while state-of-the-art LLM and AGI research relies on massive data generation, simulation, and evaluation pipelines, most existing tooling is optimized for expert engineers rather than research teams who need to iterate quickly. Matrix bridges this gap by providing a reliable, scalable, and researcher-friendly platform built to connect foundational components into a seamless experimentation workflow.
The speakers walk through Matrix’s core capabilities, including:
Auto-scaled data generation from LLMs, game engines, and physics/world-model simulators—triggered with a single command
Easy-to-use large-scale data processing and augmentation, such as batch LLM-as-a-judge evaluations, safe sandboxed code execution, deduplication, clustering, and classification
Reproducible evaluation pipelines designed for collaboration across large research teams
They also describe how Matrix integrates with industry-standard technologies such as Ray and vLLM, enabling scalable distributed compute and high-throughput inference within the framework.
Matrix is already empowering both research and production initiatives at Meta across AGI, multimodal LLMs (MLLMs), and world modeling. In this session, the team introduces Matrix’s architecture, its synergy with the broader ecosystem, and the high-impact research workflows it unlocks. They conclude with a short tutorial to help developers get started and contribute.
Attendees will learn how Matrix accelerates data-centric AI research, simplifies large-scale experimentation, and supports next-generation model development at Meta.
Liked this video? Check out other Ray Summit breakout session recordings youtube.com/playlist?list=PLzTswPQNepXllnU0C36WtkC0dqkAoDulh
Subscribe to our YouTube channel to stay up-to-date on the future of AI! youtube.com/c/anyscale
🔗 Connect with us:
LinkedIn: linkedin.com/company/joinanyscale
X: https://x.com/anyscalecompute
Website: anyscale.com
At Ray Summit 2025, Hongpeng Guo, Shang-Wen Li, Ramya Raghavendra, and Dong Wang from Meta share how the Fundamental AI Research (FAIR) team built Matrix, a powerful framework that lowers the barrier for researchers to conduct large-scale, data-centric experimentation with cutting-edge LLMs and multimodal models.
They begin by highlighting a key challenge in today’s AI landscape: while state-of-the-art LLM and AGI research relies on massive data generation, simulation, and evaluation pipelines, most existing tooling is optimized for expert engineers rather than research teams who need to iterate quickly. Matrix bridges this gap by providing a reliable, scalable, and researcher-friendly platform built to connect foundational components into a seamless experimentation workflow.
The speakers walk through Matrix’s core capabilities, including:
Auto-scaled data generation from LLMs, game engines, and physics/world-model simulators—triggered with a single command
Easy-to-use large-scale data processing and augmentation, such as batch LLM-as-a-judge evaluations, safe sandboxed code execution, deduplication, clustering, and classification
Reproducible evaluation pipelines designed for collaboration across large research teams
They also describe how Matrix integrates with industry-standard technologies such as Ray and vLLM, enabling scalable distributed compute and high-throughput inference within the framework.
Matrix is already empowering both research and production initiatives at Meta across AGI, multimodal LLMs (MLLMs), and world modeling. In this session, the team introduces Matrix’s architecture, its synergy with the broader ecosystem, and the high-impact research workflows it unlocks. They conclude with a short tutorial to help developers get started and contribute.
Attendees will learn how Matrix accelerates data-centric AI research, simplifies large-scale experimentation, and supports next-generation model development at Meta.
Liked this video? Check out other Ray Summit breakout session recordings youtube.com/playlist?list=PLzTswPQNepXllnU0C36WtkC0dqkAoDulh
Subscribe to our YouTube channel to stay up-to-date on the future of AI! youtube.com/c/anyscale
🔗 Connect with us:
LinkedIn: linkedin.com/company/joinanyscale
X: https://x.com/anyscalecompute
Website: anyscale.com
