Claim · 2020-06-30 · publication · Stated in source · Supported

The paper reports using GShard's automatic sharding to scale a multilingual neural machine translation Transformer with Sparsely-Gated Mixture-of-Experts beyond 600 billion parameters.

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What it connects

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding → states its contribution → The paper reports using GShard's automatic sharding to scale a multilingual neural machine translation Transformer with Sparsely-Gated Mixture-of-Experts beyond 600 billion parameters.

Where it appears

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Claim and evidence

GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingstates its contribution ↓The paper reports using GShard's automatic sharding to scale a multilingual neural machine translation Transformer with Sparsely-Gated Mixture-of-Experts beyond 600 billion parameters.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2020-06-30 · publication Records dated 2020 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

arXiv · 2020-06-30

Cite the original source

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Source excerpt

“GShard enabled us to scale up multilingual neural machine translation Transformer model with Sparsely-Gated Mixture-of-Experts beyond 600 billion parameters using automatic sharding.”

Location in source arXiv v1, PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Dmitry Lepikhin; HyoukJoong Lee; Yuanzhong Xu; Dehao Chen; Orhan Firat; Yanping Huang; Maxim Krikun; Noam Shazeer; Zhifeng Chen
Version: arXiv:2006.16668v1, submitted 30 June 2020 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 4078d81e9ef6db2e3a982e56d3d8816766d8d49f350e02f7410501ca496ad54e). The author list is the byline as printed on the first page.
Version date: 2020-06-30
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen, “GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding”, 2020-06-30.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (22 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 30 June 2020 (UTC); later versions may word it differently.

Record ID kp-states-gshard-scaling-giant-models-conditional-2020-2