BlockTrain partitions models into block-local diffusion objectives that train near end-to-end WikiText quality with one-block worker memory, real WAN transport, and one-sweep distributed serving.
FusionLLM : A decentralized LLM training system on geo-distributed GPUs with adaptive compression, 2024
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BACE-Pipe is a bandwidth-aware and cost-efficient pipeline scheduling framework for geo-distributed LLM training that reduces average JCT by 27.9-64.7% and electricity cost by 12.6-30.6% in simulations versus baselines.
ScaleAcross Explorer jointly optimizes three design dimensions for scale-across training and reports up to 64.62% speedups over production baselines and 37.59% over prior art in testbed and simulation experiments.
citing papers explorer
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Decentralised AI Training and Inference with BlockTrain
BlockTrain partitions models into block-local diffusion objectives that train near end-to-end WikiText quality with one-block worker memory, real WAN transport, and one-sweep distributed serving.
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Bandwidth-Aware and Cost-Efficient Pipeline Parallel Scheduling in Geo-Distributed LLM Training
BACE-Pipe is a bandwidth-aware and cost-efficient pipeline scheduling framework for geo-distributed LLM training that reduces average JCT by 27.9-64.7% and electricity cost by 12.6-30.6% in simulations versus baselines.
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ScaleAcross Explorer: Exploring Communication Optimization for Scale-Across AI Model Training
ScaleAcross Explorer jointly optimizes three design dimensions for scale-across training and reports up to 64.62% speedups over production baselines and 37.59% over prior art in testbed and simulation experiments.