# Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems

arXiv: https://arxiv.org/abs/2003.09518 · HTML: https://mlsanity.com/paper/2003.09518/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2020.

## Built on by (most cited first, 30 of 38)

- [Pathways: Asynchronous Distributed Dataflow for ML](https://mlsanity.com/paper/2203.12533/) (2022)
- [Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models](https://mlsanity.com/paper/2104.05158/) (2021)
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- [RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference](https://mlsanity.com/paper/2102.00075/) (2021)
- [RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation](https://mlsanity.com/paper/2201.10095/) (2022)
- [Accelerating Recommendation System Training by Leveraging Popular Choices](https://mlsanity.com/paper/2103.00686/) (2021)
- [Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters](https://mlsanity.com/paper/2111.05897/) (2021)
- [AutoShard: Automated Embedding Table Sharding for Recommender Systems](https://mlsanity.com/paper/2208.06399/) (2022)
- [DreamShard: Generalizable Embedding Table Placement for Recommender Systems](https://mlsanity.com/paper/2210.02023/) (2022)
- [ReCycle: Resilient Training of Large DNNs using Pipeline Adaptation](https://mlsanity.com/paper/2405.14009/) (2024)
- [Heterogeneous Acceleration Pipeline for Recommendation System Training](https://mlsanity.com/paper/2204.05436/) (2022)
- [RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure](https://mlsanity.com/paper/2211.05239/) (2022)
- [RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance](https://mlsanity.com/paper/2105.08820/) (2021)
- [Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation](https://mlsanity.com/paper/2403.00877/) (2024)
- [Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training](https://mlsanity.com/paper/2509.03018/) (2025)
- [Supporting Massive DLRM Inference Through Software Defined Memory](https://mlsanity.com/paper/2110.11489/) (2021)
- [Efficient Direct-Connect Topologies for Collective Communications](https://mlsanity.com/paper/2202.03356/) (2022)
- [Learning to Collide: Recommendation System Model Compression with Learned Hash Functions](https://mlsanity.com/paper/2203.15837/) (2022)
- [Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models](https://mlsanity.com/paper/2305.01868/) (2023)
- [DNNAbacus: Toward Accurate Computational Cost Prediction for Deep Neural Networks](https://mlsanity.com/paper/2205.12095/) (2022)
- [FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models](https://mlsanity.com/paper/2301.02959/) (2023)
- [UpDLRM: Accelerating Personalized Recommendation using Real-World PIM Architecture](https://mlsanity.com/paper/2406.13941/) (2024)
- [Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems](https://mlsanity.com/paper/2105.01064/) (2021)
- [Sparse Checkpointing for Fast and Reliable MoE Training](https://mlsanity.com/paper/2412.15411/) (2024)
- [The trade-offs of model size in large recommendation models : A 10000 × compressed criteo-tb DLRM model (100 GB parameters to mere 10MB)](https://mlsanity.com/paper/2207.10731/) (2022)
- [COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training](https://mlsanity.com/paper/2211.16648/) (2022)
- [Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training](https://mlsanity.com/paper/2508.03854/) (2025)
- [RIBBON: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances](https://mlsanity.com/paper/2207.11434/) (2022)

## Abstract (arXiv)

Large-scale training is important to ensure high performance and accuracy of machine-learning models. At Facebook we use many different models, including computer vision, video and language models. However, in this paper we focus on the deep learning recommendation models (DLRMs), which are responsible for more than 50% of the training demand in our data centers. Recommendation models present unique challenges in training because they exercise not only compute but also memory capacity as well as memory and network bandwidth. As model size and complexity increase, efficiently scaling training becomes a challenge. To address it we design Zion - Facebook's next-generation large-memory training platform that consists of both CPUs and accelerators. Also, we discuss the design requirements of future scale-out training systems.