arXiv:2605.27786v1 Announce Type: new Abstract: Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving inference efficiency. Existing one-shot pruning methods rely on local layer importance or fixed redundancy assumptions across architectures. We propose Locality-Aware Redundancy Pruning (LoRP), a training-free one-shot depth pruning framework guided by representation locality. We show that inter-layer redundancy can be either localized or globally distributed depending on the LLM architecture. To ch
The explosion of large language models and their increasing computational demands create an urgent need for efficiency improvements like depth compression, making research in this area highly relevant.
This research addresses a critical bottleneck in deploying powerful LLMs, potentially leading to more efficient, accessible, and sustainable AI, which is crucial for broad economic and technological adoption.
The proposed 'Locality-Aware Redundancy Pruning' (LoRP) framework could significantly reduce the computational footprint of LLMs without requiring extensive retraining, accelerating their deployment and reducing operational costs.
- · AI developers
- · Cloud providers
- · Edge AI hardware manufacturers
- · SaaS companies leveraging LLMs
- · Companies relying solely on brute-force scaling
- · Less efficient AI infrastructure providers
LLMs become more performant and cheaper to run, facilitating wider adoption.
Reduced computational requirements ease the demand on leading-edge compute, potentially diversifying AI infrastructure and reducing energy consumption.
More efficient LLMs could enable new applications and form factors for AI that are currently infeasible due to computational constraints.
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Read at arXiv cs.LG