Decentralised AI Training and Inference with BlockTrain
Frontier AI training is increasingly shaped by access to dense, centrally controlled accelerator clusters. This creates a structural advantage for hyperscalers and large centralized laboratories, and makes open or independent AI efforts depend on scarce capital, privileged infrastructure, and data-center geography. We present Spheroid BlockTrain, a decentralized training protocol in which a model is partitioned into independently trainable blocks, each optimized on a local objective derived from the same global target and composed at inference into one model. On byte-level WikiText, BlockTrain reaches cross entropy 1.359 (perplexity 3.89), within about 0.04 CE of a same-setup end-to-end T...
| Authors | Peter Toth, Dan Oprisa |
|---|---|
| Primary category | cs.AI |
| Published | 23 June 2026 |
| Last updated at source | 3 July 2026 |
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