Meta
Hyperscaler
Social media giant, open-source AI leader (Llama models).
Recent activity
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We’re introducing ZGateway, the proxy we are using to unify traffic through ZippyDB, Meta’s most widely-used key value store. As a bonus, it also enables admission control, load balancing, cross-region resilience, and richer operations. ZippyDB is the most widely used key value store at Meta, backing product metadata, counters, and configuration, and can serve billions [...] Read More... The post ZGateway: Learnings from Putting a Proxy in Front of ZippyDB appeared first on Engineering at Meta .
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We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon. This is not a typical domain-specific agent. Its novelty comes from integrating two layers: A structured, auditable knowledge architecture separates what [...] Read More... The post An Organizational Second Brain: Building an AI That Learns From Experts appeared first on Engineering at Meta .
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Training and serving frontier AI models depends on fast, reliable networks that move data between GPUs without wasting compute cycles. To meet this challenge at scale, Meta designed MetaRoCE – a clean-sheet RDMA transport protocol purpose-built for AI workloads on commodity Ethernet. We’re releasing the MetaRoCE specification, a reference software implementation and a compliance test [...] Read More... The post MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet appeared first on Engineer
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MTIA 300 is the first of Meta’s family of in-house training and inference accelerators optimized for training ranking and recommendation models. We’re sharing how MTIA 300’s built-in NIC chiplets allow it to meet the communication needs associated with training recommendation models with superior performance over general-purpose GPUs. By co-designing MTIA’s communication library, HCCL, alongside the [...] Read More... The post MTIA 300: Meta’s First Training Chip with Built-in NICs and Communica
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WhatsApp is committed to helping people stay safe while protecting the privacy of their messages. As scam tactics evolve — from impersonation to social engineering to AI-generated lures — we’re always evolving as well, so that our protections stay ahead of scammers while protecting people’s personal messages with end-to-end encryption. Today, we’re sharing an early [...] Read More... The post How We’re Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees appear
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Every day, Meta’s recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content. In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timing of user actions (rather than relying on static, manually engineered sparse features) [...] Read More... The post From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranki
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Meta’s Generative Ads Recommendation Model (GEM), the foundation model behind ads recommendations across Instagram and Facebook, now trains at LLM scale on several thousand of the latest-generation GPUs. This post goes into the details on how we achieved: doubling end-to-end (E2E) training efficiency to 20–25% Model FLOPs Utilization (MFU) while scaling training FLOPs 4x in [...] Read More... The post GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model appeared fi
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Hierarchical Interest Representation is a research area for Meta Ads. We’re exploring an upstream representation layer over the universe of Ads entities – users, advertisers, products, services – learning unified embeddings that connect users’ inferred interests with the breadth of what advertisers offer in their deep funnel ads. The innovations in Hierarchical Interest Representation are [...] Read More... The post Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Op
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TL; DR At Meta’s scale, a few milliseconds of latency degradation can have a significant negative impact on ads performance. When a Linux kernel upgrade risked regressing latency across Meta’s ad serving fleet, we turned to sched_ext — the upstream, BPF-based extensible scheduling framework — to build a scheduling policy customized to the Ads delivery [...] Read More... The post Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler appeared first on Engineering at Met
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Over the past several years, model capabilities and training dataset sizes have experienced exponential growth. During the past year or so, the time between new-frontier-model releases has gone down from months to weeks. Reliable and fast access to storage is important to both the speed and computational cost of this AI innovation. If AI is [...] Read More... The post Meta’s AI Storage Blueprint at Scale appeared first on Engineering at Meta .
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This year marks Meta’s 10th consecutive year as a sponsor of the Python Software Foundation (PSF), the charitable organization dedicated to advancing, supporting, and protecting the open-source Python programming language and the community that sustains it. Python is one of the world’s most influential programming languages, and we use it across our engineering stack, from [...] Read More... The post 10 Years of Meta’s Commitment to Python appeared first on Engineering at Meta .
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Privacy controls — systems that enforce retention, access, allowed-purpose, downstream-sharing, or anonymization policies — require a reliable understanding of data to function. Before such a control can operate effectively, it must know exactly what it is looking at. This can be complex, as demonstrated by a field simply named “age“: In one context, it [...] Read More... The post Privacy-Aware Infrastructure in the AI-Native Era: An Asset Classification Case Study appeared fi
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Smart glasses like the Ray-Ban Meta and Oakley Meta Vanguards need to pack enough energy to power features like cameras, speakers, AI workloads, and even a display. But it all has to fit into the glasses’ temple arms. So how do you place a battery with enough power to run a pair of smart glasses [...] Read More... The post How Meta Engineered Ultra-Narrow Batteries for AI Glasses appeared first on Engineering at Meta .
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Adopting AV1 for real-time communication at Meta has been a multi-year effort spanning codec selection, device eligibility, rate control, and error resilience. We’re sharing the technical and operational challenges while deploying AV1 and expanding coverage, and how we addressed them for real-time communication. We’re presenting several technologies for improving AV1 call quality, including rate control [...] Read More... The post Adopting AV1 for Real-Time Communication (RTC) at Scale appeared
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We’re introducing Instantaneous PowerLoss Storm, a new testing paradigm within Meta’s infrastructure for handling and mitigating instant or zero-notice power loss in our data centers. We’re sharing: how we built readiness to tolerate instant failures into our existing systems with defense-in-depth strategies; tradeoffs made in implementing it, and how we validated our readiness. Disaster preparedness [...] Read More... The post Lights Out, Systems On: Validating Instant Power Loss Readiness appe
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We’re introducing SilverTorch, a reimagining of recommendation systems that unifies all retrieval components for user generated content under a unified architecture. SilverTorch shows up to 23.7x higher throughput compared to the state-of-the-art approaches. It’s also showing 20.9x more compute cost efficiency compared to a CPU-based solution while also improving accuracy. Our research paper, “SilverTorch: A [...] Read More... The post SilverTorch: Index as Model — A New Retrieval Paradigm for R
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