DiLoCo Explained: Training AI Without Datacenters
How DiLoCo enables distributed AI training across consumer hardware with minimal communication overhead.
VoidAI Team
VoidAI
DiLoCo (Distributed Low-Communication) is a training paradigm from Google DeepMind that changes everything about distributed AI training.
The Traditional Approach
Standard distributed training requires constant synchronization. Every gradient update needs to be shared across all nodes. This works in a datacenter with fast interconnects, but falls apart over the internet.
How DiLoCo Works
DiLoCo flips the script:
- 1. Each node trains independently for N steps (inner optimization)
- 2. Nodes periodically sync compressed gradients (outer optimization)
- 3. A global model emerges from the aggregate
The key insight: you don't need constant synchronization. Local training with periodic merging works surprisingly well.
Why This Matters
With DiLoCo: - Nodes can be on different continents - Connection drops don't kill training - Consumer GPUs become viable compute - Costs drop by 90%+
Our Implementation
VoidAI Train uses DiLoCo with several enhancements: - Adaptive compression (100-1000x) - Fault tolerance for node failures - Reactive orchestration via Blitz Engine - P2P mesh for direct node communication
More details in our technical docs.