Pretraining A Large Language Model using Distributed GPUs: A Memory-Efficient Decentralized Paradigm
arXiv:2602.11543v3 Announce Type: replace Abstract: Pretraining large language models (LLMs) typically requires centralized clusters with thousands of high-memory GPUs (e.g., H100/A100). Recent decentralized training methods reduce communication overhead by employing federated optimization; however, they still need to train the entire model on each node, remaining constrained by GPU memory limitations. In this work, we propose SParse Expert Synchronization (SPES), a memory-efficient decentralized framework for pretraining mixture-of-experts (MoE) LLMs. SPES trains only a subset of experts per
The increasing scale of LLMs is pushing the limits of current centralized compute infrastructure, driving innovation in more distributed and memory-efficient training paradigms.
This development could significantly lower the barrier to entry for training large AI models, reducing reliance on hyper-scale centralized GPU clusters and potentially democratizing AI development.
The ability to pretrain LLMs more efficiently on decentralized and less powerful hardware removes a key bottleneck, opening up new possibilities for AI research and deployment outside of dominant data centers.
- · AI startups
- · Academic researchers
- · Open-source AI community
- · Distributed computing platforms
- · Cloud providers reliant solely on centralized high-end GPU offerings
- · Nations with limited access to top-tier compute resources
Reduced cost and increased accessibility for training large language models.
Acceleration of AI development and diversification of AI models beyond a few large players.
Potential for new business models around decentralized AI training and more robust, resilient AI infrastructure.
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