meta-pytorch/torchrec

Health v2 · maintenance only

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2599 stars · 684 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TorchRec is a PyTorch domain library for building recommendation systems at scale. It provides distributed sharding of large embedding tables and GPU-accelerated training and inference for recommender models.

Use cases

• train large recommendation models on multiple gpus
• shard huge embedding tables across a cluster
• build a recommender system with pytorch
• run distributed training for deep learning recommendation models
• serve recommendation model inference efficiently on gpu

When to choose

• you need to scale embedding tables beyond a single gpu's memory
• you are building recommender models in the pytorch ecosystem
• you want optimized gpu kernels for sparse embeddings

When to avoid

• you need a simple non-distributed recommender prototype
• your stack is tensorflow-based rather than pytorch
• you need a turnkey recommendation service rather than a training library

Facets

library · maturity active

deep-learning machine-learning gpu-computing machine-learning deep-learning gpu-computing python pytorch recommender-systems sharding distributed-training embedding-tables recommendation-systems gpu linux cuda

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