# Meta
> Meta Platforms is the parent corporation that owns major social and communication platforms including Facebook, Instagram, WhatsApp, and Threads.

## Articles

### [MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet](https://yomu.fyi/post/metaroce-a-new-rdma-transport-built-for-ai-scale-ethernet.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Arvind Srinivasan, Neil Spring, Omar Baldonado, Rajiv Krishnamurthy
- Published: Aug 24, 2026

Scaling frontier AI training and inference across massive GPU clusters places severe demands on network fabrics, yet conventional RoCE relies on Priority Flow Control to enforce in-order packet delivery. Meta introduced MetaRoCE, a clean-sheet RDMA transport protocol engineered specifically for lossy, multiplane commodity Ethernet without requiring PFC. By shifting intelligence to endpoint NICs, the protocol sprays packets across fine-grained logical paths, writing out-of-order data directly into destination memory without reorder buffers or head-of-line blocking. Congestion is managed via per-path ECN windows paired with receiver-driven rate hints returned in acknowledgments. In validation on a 64-node AMD GPU cluster running collective operations, MetaRoCE outperformed RoCEv2, sustaining roughly 86% throughput under 1% packet loss and achieving linear scaling across multiplane fabrics.


### [MTIA 300: Meta’s First Training Chip with Built-in NICs and Communication-Offloading Engines](https://yomu.fyi/post/mtia-300-meta-s-first-training-chip-with-built-in-nics-and-communicati.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Rajiv Krishnamurthy, Wes Bland
- Published: Aug 24, 2026

Training recommendation models poses distinct network bottlenecks because massive embedding tables trigger frequent collective operations that compete with computation on standard GPUs. Meta designed the MTIA 300 accelerator to overcome this by embedding two network chiplets containing twelve 800 Gbps RDMA NICs directly inside the chip package. The architecture offloads communication from its compute grid to sixteen dedicated message engines equipped with RISC-V cores and near-memory compute reduction blocks. Working alongside the co-designed HCCL library, the chip autonomously compiles and executes collective communication subgraphs without host CPU intervention. On a 150-billion-parameter production recommendation model, MTIA 300 delivered communication speeds 3.9 times faster than an equivalent GPU cluster while keeping concurrent compute degradation under 0.5 percent.


### [How We’re Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees](https://yomu.fyi/post/how-we-re-building-scam-alert-on-whatsapp-with-end-to-end-encryption-a.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Published: Aug 12, 2026

WhatsApp is introducing Scam Alert, an optional feature designed to detect scam messages from non-contacts while preserving end-to-end encryption guarantees. The system executes text classification entirely on-device using a machine learning model trained on conversational patterns from user reports, ensuring raw message content never leaves the client. To evaluate feature accuracy without compromising privacy, the client transmits minimal pre-aggregated telemetry—consisting only of warning and action counts—over Oblivious HTTP relays to Trusted Execution Environments. These confidential virtual machines securely aggregate metrics and apply differential privacy noise before exposing population-level statistics to servers. System integrity and non-targetability are maintained by requiring clients to attest backend binaries against public transparency ledgers before transmitting any telemetry.


### [From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking](https://yomu.fyi/post/from-user-sequences-to-scaling-laws-a-multi-stage-architecture-for-met.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Steven De Gryze, Parshva Doshi, Sean O'Byrne, Arnold Overwijk, Dinesh Ramasamy, Lee Xiong
- Published: Aug 5, 2026

Scaling sequence models for real-time ad recommendations faces latency bottlenecks and scaling ceilings when hybrid configurations process both long user histories and sparse feature interactions online. Meta addresses these trade-offs with a multi-stage architecture that decouples heavy, asynchronous offline user modeling from lightweight, real-time online ranking. Deep upstream transformer models process sequences of thousands of actions to generate cached user embeddings, which downstream online models combine with candidate ad features using dense tokenization and target-aware multi-head attention. This framework produces predictable, LLM-style log-linear scaling curves between compute and normalized entropy without disproportionate increases in online serving costs. Implemented as part of Meta's Generative Ads Recommendation Model, the design achieved a cumulative 6% lift in Instagram conversions and a 3% lift in Facebook conversions.


### [GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model](https://yomu.fyi/post/gem-training-how-meta-doubled-the-efficiency-of-its-llm-scale-ads-foun.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Published: Aug 3, 2026

Meta's Generative Ads Recommendation Model (GEM) trains across thousands of GPUs using a hybrid architecture featuring trillions of sparse embedding parameters and billions of dense parameters. Standard training infrastructure built for large language models struggles with recommendation-specific workloads characterized by jagged user activity histories, asymmetric attention patterns, and numerical sensitivity. To overcome these bottlenecks, engineering teams introduced custom kernels like Jagged Flash Attention and Generalized Dot-Product Attention alongside MXFP8 mixed-precision execution. Pairing these kernel optimizations with topology-aware 5D parallelism doubled end-to-end training efficiency to 20–25% Model FLOPs Utilization while scaling total training FLOPs fourfold over twelve months.


### [Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization](https://yomu.fyi/post/exploring-hierarchical-interest-representation-for-meta-ads-deep-funne.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Yuhui Ouyang, Di Wang, Sreedal Menon, Jie Tian
- Published: Jul 15, 2026

Meta developed Hierarchical Interest Representation as an upstream representation layer to optimize deep funnel ad ranking across its platforms. The architecture organizes interactions among users, advertisers, and products into a heterogeneous, time-decayed graph and projects sparse raw edges into multi-level super-graphs of latent interest primitives. To encode these structures, a transformer-based hierarchical encoder incorporates multimodal product semantics processed via language models alongside topology-aware attention biases. Computation scales across billions of entities by pairing deep hash ID embeddings with FlexAttention to evaluate graph biases on the fly without materializing full matrices. Self-supervised cross-view distillation trains student representations against broader teacher graph views to yield universal embeddings and interest tokens.


### [Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler](https://yomu.fyi/post/modernizing-the-meta-ads-service-with-an-open-source-kernel-scheduler.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Published: Jul 13, 2026

Meta's ad serving fleet encountered latency regressions and technical debt after Linux kernel upgrades introduced the default EEVDF scheduler. To resolve these performance drops across billions of daily requests, the engineering team implemented sched\_ext, an open-source, BPF-based scheduling framework integrated into Linux kernel v6.12. The custom scheduling policy uses domain knowledge to soft-partition CPUs dynamically into separate pools for latency-critical request threads and background work, enhancing L3 cache locality. Packaged as a user-space binary, the scheduler allows policy iterations and rollouts via process restarts without kernel rebuilds. The deployment achieved a 28% reduction in p99 ads retrieval latency, a 1.1% increase in ranked ads, and 3.28 megawatts in fleet-wide power savings.


### [Meta’s AI Storage Blueprint at Scale](https://yomu.fyi/post/meta-s-ai-storage-blueprint-at-scale.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Sidharth Bajaj, Venkatraghavan Srinivasan
- Published: Jul 1, 2026

Meta redesigned its BLOB-storage architecture on top of the foundational Tectonic block layer to eliminate GPU stalls and accelerate AI training workflows. The legacy storage system suffered from multi-layered metadata lookups, cross-region latency, and dataplane proxy bottlenecks that conflicted with the millisecond access requirements of flash-based AI clusters. To resolve these issues, the team collapsed disparate metadata into a single flat schema backed by ZippyDB for O(1) path lookups and introduced a fat client SDK capable of streaming data directly from storage servers. The updated stack also leverages spare GPU host memory as a distributed data cache alongside distributed read-plan caches to handle severe traffic spikes during checkpointing and model loading. These protocol and architectural changes deliver predictable latencies, reduce power consumption, and maintain high throughput during distributed training.


### [10 Years of Meta’s Commitment to Python](https://yomu.fyi/post/10-years-of-meta-s-commitment-to-python.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Published: Jun 30, 2026

Meta has marked ten consecutive years as a sponsor of the Python Software Foundation (PSF) to ensure the language remains robust and sustainable. Python represents the most widely used programming language across Meta's infrastructure, powering applications like Instagram and Threads as well as internal AI research. Beyond financial backing, Meta engineers actively contribute core language features, author Python Enhancement Proposals (PEPs), and build open-source tools such as the Pyrefly type checker. Company sponsorship directly funds the PSF's Developer-in-Residence program, core infrastructure, and security enhancements for the Python Package Index (PyPI). Continued support of the PSF functions as a strategic investment in maintaining the stability of the broader open-source ecosystem.