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Intelligent Delivery: Making Every Unit of Compute Serve Better Ads - Engineering at Meta

Ads ranking models are growing toward LLM scale faster than the hardware to run them can keep up.
An extra unit of compute has more impact on some ad requests, models, and chips than on others.
The challenge is deciding where to allocate it to get the best outcome for the entire system.

engineering.fb.com3d ago

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How We Built TLX: A Hardware-Native GPU Compiler for Production ML at Scale - Engineering at Meta

Triton Low-Level Extensions (TLX) , is an extension to Triton that helps engineers optimize GPU performance while preserving Triton’s productive…
TLX introduces explicit warp-group and cluster-level orchestration, letting developers adopt new hardware capabilities without sacrificing velocity.
TLX has demonstrated performance improvements in RecSys training, RecSys inference, and large-scale LLM pretraining.

engineering.fb.com4d ago

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Extending LLDB to Debug Applications That Span CPUs and GPUs - Engineering at Meta

We built a vendor-neutral accelerator plugin architecture in lldb-server for debugging applications that span CPUs and GPUs.
It separates shared debugger infrastructure from hardware-specific device control.
The design represents the CPU and GPU as separate debugger processes while presenting them in one debugging session.

engineering.fb.com5d ago

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F3 Lightning: How Meta Accelerates Ads ML Features to Production at Scale - Engineering at Meta

Facebook Feature Framework (F3) is Meta’s framework for authoring, compiling, and serving the machine learning features underlying ads ranking models…
We first presented F3 at AI @ Scale 2020 .
F3 reduced execution computational costs by compiling all features into a single merged graph…

engineering.fb.com11d ago

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Catching Bugs Before They Land: JiTTesting’s Human-on-the-Loop Approach at Full Scale - Engineering at Meta

Over the last year we’ve been sharing our work leveraging large language models to catch bugs on the fly —…
Now, we’re sharing the results and positive impact we’ve seen with Catching JiTTests deployed into production.
Our paper, “ Just-in-Time Catching Test Generation at Meta ,” fully details our initial deployment and testing with Catching JiTTests.

engineering.fb.com16d ago

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Meta Andromeda: Powering the Next Generation of Ads Retrieval Through Model-System Co-Design - Engineering at Meta

This post describes the next generation of Meta Andromeda, Meta’s personalized ads retrieval engine, and how co-designing the model, system…
In production, the system scores every eligible ad with near ranking-level model performance at retrieval scale and surfaces the top…
For advertisers, this means better performance automatically from the retrieval system matching ads across a larger candidate pool…

engineering.fb.com17d ago

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PrivacyLib: Privacy Controls at the Data Access Boundary - Engineering at Meta

This latest deep dive into Meta’s Privacy Aware Infrastructure (PAI) takes a look at PrivacyLib.
A privacy requirement must hold as data moves through Meta’s diverse data and processing systems.
Historically, supporting a new requirement meant implementing the same enforcement logic separately in each system.

engineering.fb.com18d ago

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Python Typing Survey 2026: AI Tools and the Shifting Checker Landscape - Engineering at Meta

The 2026 Python Typing Survey received 1,794 responses.
Type hint use changed little across three years, but checker selection changed sharply.
Among respondents who reported which checkers they use, half selected more than one…

engineering.fb.com18d ago

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Meta Networking at the 2026 OCP Global Summit: Open Innovation for Scale-Up, Scale-Out, and Scale-Across AI Networks - Engineering at Meta

At the 2026 Open Compute Project (OCP) Global Summit…
IcePack & IceCube: Meta’s first liquid-cooled network rack with 102.4T switch blades (128×800G, Broadcom Tomahawk 6C and NVIDIA Spectrum-6 variants)…
Flattened Planar Fabric (FPF) for scale-out: Meta’s multi-plane scale-out fabric where each GPU connects to eight network planes.

engineering.fb.com19d ago

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GenRecs Tokenization: Transforming Ads Recommendation at Meta with Stable Model Vocabulary - Engineering at Meta

Generative Recommenders (GenRecs) represent a paradigm shift from scoring every ad to reasoning about ad content and user preferences together in order to predict the best outcome for advertisers.
Leveraging the world knowledge built into LLMs makes ad matching more intelligent and precise, compounding gains for advertisers.
GenRecs Tokenization is the foundational data representation that makes this possible.

engineering.fb.com26d ago

Tech

The Thin Orchestrator: How We Made Our AI Agents More Reliable by Giving the Router Less to Do - Engineering at Meta

Sometimes, giving an AI agent all of the knowledge it needs is the wrong approach.
At Meta, we’ve seen notable improvements in efficiency and accuracy by keeping the orchestration layer of our data pipeline agents deliberately thin: It routes work and enforces reliability checks, while specialized age…
We’re sharing our own process and insights, plus how any team can apply this to their production AI agents.

engineering.fb.com26d ago

Tech

Inside Prometheus: Meta’s Journey to a Gigawatt-Scale Supercluster - Engineering at Meta

With the recent release of the Muse family of foundational models, we want to take you inside the infrastructure that has made it all possible.
The first phase of Prometheus, our gigawatt-scale supercluster, is already complete and we’re working toward scaling it to comprise ~1 million GPUs.
Prometheus also features technologies like closed-loop liquid cooling to help it operate efficiently.

engineering.fb.com26d ago

Tech

How Meta Built Forum With AI-Native Engineering - Engineering at Meta

Meta’s newest app, Forum, is for anyone who wants to go deeper into the conversations in their Facebook groups.
To build Forum, Meta engineers adopted a whole new way of working by bringing an AI agent into the development cycle.
We’re sharing insights into what it took to build Forum with agentic AI: how we adapted the codebase, tested frameworks and internal tooling for agentic execution, what worked, and what didn’t.

engineering.fb.com30d ago

Tech

2026 World Cup: Breaking Records, Spiky Charts and Reliability at Scale - Engineering at Meta

Every four years, the World Cup becomes the largest planned stress test of our infrastructure.
This year it also set an all-time record!
There are a few moments throughout the year, like New Years Eve, that really let us see the collective impact of everything we’ve built — but none are bigger than the quadrennial World Cup tournament.

engineering.fb.com30d ago

Tech

Sapienz Receives ISSTA 2026 Impact Paper Award - Engineering at Meta

Ke Mao, Mark Harman, and Yue Jia have been recognized with the ACM SIGSOFT International Symposium on Software Testing and Analysis ( ISSTA) 2026 Most Influential Paper Award for their 2016 research, “ Sapienz: multi-ob…
” The recognition celebrates a decade of impact from work that began at University College London and went on to become testing infrastructure at Meta.
Sapienz automatically tests apps to find crashes and helps engineers reproduce them in fewer steps, making failures easier to investigate.

engineering.fb.com30d ago

The Daily Digest

The Daily Digest

Tech

Cuttlefish on Mac: Running Android's Virtual Device Framework Natively on Apple Silicon - Engineering at Meta

Intelligent Delivery: Making Every Unit of Compute Serve Better Ads - Engineering at Meta

Your Algorithm: Giving People More Ways to Personalize Facebook Recommendations - Engineering at Meta

How We Built TLX: A Hardware-Native GPU Compiler for Production ML at Scale - Engineering at Meta

Extending LLDB to Debug Applications That Span CPUs and GPUs - Engineering at Meta

F3 Lightning: How Meta Accelerates Ads ML Features to Production at Scale - Engineering at Meta

Catching Bugs Before They Land: JiTTesting’s Human-on-the-Loop Approach at Full Scale - Engineering at Meta

Meta Andromeda: Powering the Next Generation of Ads Retrieval Through Model-System Co-Design - Engineering at Meta

PrivacyLib: Privacy Controls at the Data Access Boundary - Engineering at Meta

Python Typing Survey 2026: AI Tools and the Shifting Checker Landscape - Engineering at Meta

Meta Networking at the 2026 OCP Global Summit: Open Innovation for Scale-Up, Scale-Out, and Scale-Across AI Networks - Engineering at Meta

GenRecs Tokenization: Transforming Ads Recommendation at Meta with Stable Model Vocabulary - Engineering at Meta

The Thin Orchestrator: How We Made Our AI Agents More Reliable by Giving the Router Less to Do - Engineering at Meta

Inside Prometheus: Meta’s Journey to a Gigawatt-Scale Supercluster - Engineering at Meta

How Meta Built Forum With AI-Native Engineering - Engineering at Meta

2026 World Cup: Breaking Records, Spiky Charts and Reliability at Scale - Engineering at Meta

Sapienz Receives ISSTA 2026 Impact Paper Award - Engineering at Meta

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