# Lagom: Unleashing the Power of Communication and Computation Overlapping for Distributed LLM Training

arXiv: https://arxiv.org/abs/2602.20656 · HTML: https://mlsanity.com/paper/2602.20656/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [Analytical Resource Management for Fine-grained MoE Computation-Communication Overlap](https://mlsanity.com/paper/2609.07536/) (2026)

## Abstract (arXiv)

Overlapping communication with computation is crucial for distributed large-model training, yet optimizing it - especially when computation becomes the bottleneck-remains challenging. We present Lagom, a system that co-tunes communication parameters to balance resource usage between computation and communication. By introducing a unified cost model and a priority-based search algorithm, Lagom reduces optimization complexity from exponential to linear. Evaluations on high- and low-bandwidth GPU clusters show that Lagom achieves 1.07-1.33x and 1.03-1.27x speedup over NCCL and AutoCCL across diverse models and parallelizations.