Methods for Organizing Distributed Training of Large Language Models in Cloud Environments

Citation: Dhaval Shah, "Methods for Organizing Distributed Training of Large Language Models in Cloud Environments", Universal Library of Engineering Technology, Volume 03, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Cloud environments make distributed LLM training a coupled systems problem: scaling forces partitioning, partitioning amplifies communication costs, and transient capacity elevates the cost of recovery. Coordinating model architecture choices with the communication stack and failure-handling policy is therefore necessary to preserve throughput under churn. In this setting, data, pipeline, and tensor parallelism are most effective when communication is overlapped with computation and checkpoint intervals are adapted to infrastructure behavior. This combination enables the use of low-cost unreliable instances without extending end-to-end training time, yielding resilient distributed training pipelines on elastic cloud platforms.

Keywords: Large Language Models, Distributed Training, Cloud Computing, Hybrid Parallelism, Pipeline Parallelism, Communication Optimization, Fault Tolerance, Checkpointing, Kubernetes Orchestration, MLOps.

https://doi.org/10.70315/uloap.ulete.2026.0301013