arXiv:2606.10706v1 Announce Type: new Abstract: Resource constraints increasingly determine what can be trained, fine-tuned, and deployed in large language models (LLMs), yet efficiency is often studied through isolated techniques rather than as an interacting system of limits. This survey adopts a constraint-centric perspective and organizes recent progress around three coupled bottlenecks: data efficiency (what to train on), memory efficiency (how to fit training), and compute budget awareness (when and where to spend FLOPs). On the data axis, we review selection and pruning methods that max
The rapid scaling of LLMs has exposed critical bottlenecks in data, memory, and compute resources, making efficiency a pressing concern for continued progress.
Efficiency in LLM training directly impacts accessibility, cost, and the ability to develop more powerful and specialized AI models, influencing the strategic landscape of AI development.
The focus is shifting from pure model size to a more holistic understanding and systematic optimization of the entire LLM training pipeline, encompassing data, memory, and compute.
- · AI efficiency researchers
- · Cloud providers with optimized infrastructure
- · Companies with proprietary, highly curated datasets
- · AI startups with lean operational models
- · Unsustainable 'train larger models' approaches
- · Developers lacking deeply optimized workflows
- · Budget-constrained AI research initiatives
- · Commodity hardware providers without efficiency solutions
More efficient LLM training will lower the barrier to entry for developing and fine-tuning advanced AI models.
This democratized access could accelerate innovation in specialized AI applications and reduce reliance on a few dominant AI labs.
Increased efficiency might alleviate some pressure on energy grids and semiconductor supply chains, though overall demand will likely continue to rise.
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