Streaming DiLoCo introduces a novel approach that allows geographically distant accelerators to efficiently train large language models by optimizing communication patterns.

• Enables distributed training across accelerators without requiring them to be physically co-located
• Reduces communication bandwidth requirements by overlapping computation and communication
• Achieves nearly identical training quality while supporting higher latency tolerance
• Demonstrates practical viability with minimal performance loss even with 500ms latency between workers

This engineering breakthrough addresses a critical bottleneck in AI infrastructure, potentially allowing organizations to leverage distributed computing resources regardless of geographic constraints.

Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch