arXiv:2602.00942v3 Announce Type: replace Abstract: Modern large language models are increasingly deployed under compute and memory constraints, making flexible control of model capacity a central challenge. While sparse and low-rank structures naturally trade off capacity and performance, existing approaches often rely on heuristic designs that ignore layer and matrix heterogeneity or require model-specific architectural modifications. We propose SALAAD, a plug-and-play framework applicable to different model architectures that induces sparse and low-rank structures during training. By formul
The increasing scale of large language models is making their deployment under real-world compute and memory constraints a critical bottleneck, driving innovation toward more efficient architectures.
Efficient and flexible control over LLM capacity enables broader deployment in resource-constrained environments, potentially expanding the reach and impact of advanced AI without requiring ever-increasing compute.
This research introduces a plug-and-play framework for inducing sparse and low-rank structures in LLMs during training, offering a generalized approach to optimize model deployment regardless of architecture.
- · AI developers
- · Edge computing providers
- · Organizations with limited compute resources
- · Companies reliant on brute-force scaling of LLMs
- · Legacy AI inference hardware
Reduced computational and memory requirements for deploying large language models.
Accelerated adoption of advanced AI in a wider range of applications and devices.
Increased competition in AI model development as efficiency becomes a key differentiator alongside raw scale.
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Read at arXiv cs.LG