What is recorded here?
Partitioned optimizer states, gradients and model parameters across data-parallel workers to reduce replicated training memory.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Communication and memory tradeoffs depend on the ZeRO stage. The title's trillion-parameter target is not itself an experimental training result.
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People and systems
Samyam Rajbhandari; Jeff Rasley; Olatunji Ruwase; Yuxiong He
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Rajbhandari and colleagues: ZeRO distributed model-state partitioning
Partitioned optimizer states, gradients and model parameters across data-parallel workers to reduce replicated training memory.
Scientific scope
Communication and memory tradeoffs depend on the ZeRO stage. The title's trillion-parameter target is not itself an experimental training result.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Samyam Rajbhandari; Jeff Rasley; Olatunji Ruwase; Yuxiong He
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.