SparseLoCo: Communication-Efficient LLM Training with Extreme Compression via Sparsification and Quantization

synapz_org

9mo ago· 2 min readenInsight

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Summary

SparseLoCo is a new communication-efficient training algorithm for Large Language Models (LLMs) that combines Top-k sparsification and quantization to achieve extreme compression ratios of 1-3% sparsity and 2-bit quantization. The method addresses communication bottlenecks in distributed LLM training across bandwidth-constrained environments like data centers and the internet, outperforming full-precision DiLoCo while reducing communication costs significantly.

Key quotes

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Communication-efficient distributed training algorithms have received considerable interest recently due to their benefits for training Large Language Models (LLMs) in bandwidth-constrained settings

Despite reducing communication frequency, these methods still typically require communicating a full copy of the model's gradients-resulting in a communication bottleneck even for cross-datacenter links

SparseLoCo provides significant benefits in both performance and communication cost

Our key observations are that outer momentum can be locally approximated by an error feedback combined with aggressive sparsity and that sparse aggregation can actually improve model performance

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Communication-efficient distributed training algorithms have received considerable interest recently due to their benefits for training Large Language Models (LLMs) in bandwidth-constrained settings, such as across data centers and over the internet. Desp

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