• We own the company's overall strategy for exploring innovative hardware technologies for CPUs, GPUs, memory, storage, and Meta's custom AI chips. We directly productionize them in Meta's hyperscale fleet of O(1,000,000) servers and O(100,000) GPUs, powering all Meta products such as Facebook, Instagram, and meta.ai. 
• We apply novel software optimizations across the whole stack---from ML models and applications to the Linux kernel---to achieve optimal performance on the hardware. 
• We develop innovative AI technologies for large language models (Llama), ranking systems, and more. 

• MTIA: First Generation Silicon Targeting Meta's Recommendation Systems
• The Grand Teton AI Server

• Deep Learning Recommendation Model for Personalization and Recommendation Systems
• Wukong: Towards a Scaling Law for Large-Scale Recommendation
• FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

• Microscaling Data Formats for Deep Learning
INT4 Decoding GQA CUDA Optimizations for LLM Inference
• Pruning and Distillation to Enable Llama 3.2 1B and 3B Models Suitable for Mobile Devices
• PyTorch Distributed Shampoo Winning the MLCommon Training Algorithms Competition

• Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression
• Training Deep Learning Recommendation Model with Quantized Collective Communications

• DCPerf: An open source benchmark suite for hyperscale compute applications
• DLRM: An advanced, open source deep learning recommendation model

• TPP: Transparent Page Placement for CXL-Enabled Tiered-Memory
• SoftSKU: Optimizing Server Architectures for Microservice Diversity @Scale

Like research labs, our team consists primarily of PhDs, and we strongly encourage and excel in research publications. However, we differ from traditional research labs in several key ways:

• Production systems: Our primary goal is to develop forward-looking innovations in AI, hardware, and software, and directly implement them in production systems that serve billions of people. The billions of users of Meta products and Meta's hyperscale fleet of O(1,000,000) servers and O(100,000) GPUs are, in effect, our lab. In contrast, traditional research labs often rely on technology transfer for a less direct impact.
• Direct ownership: Like traditional research labs, we build strong partnerships with numerous teams across diverse areas for broad influence. However, what sets us apart is our direct ownership of the hardware strategy for Meta's hyperscale fleet. This enables us to lead in many areas while fostering seamless partnerships in others.
• Impact: Our impact is widely recognized across the company. We drive Meta's hardware strategy to save billions of dollars, and directly develop innovative technologies in Meta's flagship products like Llama and Ads ranking models.