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ZeRO-Offload

4 papers tagged archive 2025-07-28

Introduced by Jie Ren et al. in ZeRO-Offload: Democratizing Billion-Scale Model Training

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

ZeRO-Offload is a sharded data parallel method for distributed training. It exploits both CPU memory and compute for offloading, while offering a clear path towards efficiently scaling on multiple GPUs by working with ZeRO-powered data parallelism. The symbiosis allows ZeRO-Offload to maintain a single copy of the optimizer states on the CPU memory regardless of the data parallel degree. Furthermore, it keeps the aggregate communication volume between GPU and CPU, as well as the aggregate CPU computation a constant regardless of data parallelism, allowing ZeRO-Offload to effectively utilize the linear increase in CPU compute with the increase in the data parallelism degree.

PaperSource

Papers archive 2025-07-28

4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
CPU4
GPU4
Computational Efficiency1
Large Language Model1
Management1
model1

Usage over time archive 2025-07-28

Papers per year tagged with ZeRO-Offload: 2021 to 2025, peak 2 2 0 2021: 1 paper 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 1 paper 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Sharded Data Parallel MethodsData Parallel MethodsDistributed Methods

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