# Decoupled DiLoCo: A new frontier for resilient, distributed AI training

DevFeed: [Decoupled DiLoCo: A new frontier for resilient, distributed AI training](<https://devfeed.tech/articles/decoupled-diloco-a-new-frontier-for-resilient-distributed-ai-training-6145.md>)

Original publisher: [Read original article](<https://deepmind.google/blog/decoupled-diloco/>)

Author: Arthur Douillard and the DiLoCo team

Published: 2026-04-22T10:20:03Z

Content type: article

Language: en

Sources: [Google DeepMind News](<https://devfeed.tech/sources/google-deepmind-news.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [Resilience](<https://devfeed.tech/topics/resilience.md>), [data centers](<https://devfeed.tech/topics/data-centers.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [ai-training](<https://devfeed.tech/tags/ai-training.md>), [communication](<https://devfeed.tech/tags/communication.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data-centers](<https://devfeed.tech/tags/data-centers.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [networking](<https://devfeed.tech/tags/networking.md>), [performance](<https://devfeed.tech/tags/performance.md>), [research](<https://devfeed.tech/tags/research.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [scale](<https://devfeed.tech/tags/scale.md>), [tpu](<https://devfeed.tech/tags/tpu.md>), [train](<https://devfeed.tech/tags/train.md>)

## AI overview

Google describes Decoupled DiLoCo, a resilient distributed training architecture that trained a 12-billion-parameter model across four U.S. regions using achievable wide-area connectivity. By overlapping communication with computation, it was reported to be more than 20 times faster than conventional synchronization and could continue operating despite failures.

## Source excerpt

Google's new distributed architecture keeps AI training runs on track across distant data centers, with exceptional efficiency - even when hardware fails.