arXiv:2602.00161v2 Announce Type: replace-cross Abstract: In this paper, we formulate the compression of large language models (LLMs) by optimally deleting transformer blocks (``block removal'') as a constrained binary optimization (CBO) problem that can be mapped to a physical system (Ising glass), whose energies are a strong proxy for downstream model performance. This formulation enables an efficient ranking of a large number of candidate block-removal configurations yielding many high-quality, non-trivial solutions beyond those only removing consecutive regions. Our method performs strongl
The proliferation of LLMs and the increasing computational demands they place on infrastructure make efficient compression methods critical for broader adoption and deployment.
This research provides a novel, more effective method for compressing LLMs, which directly addresses the high computational and energy costs associated with large models.
The ability to significantly compress LLMs without substantial performance degradation alters the cost-benefit analysis for deploying these models, potentially enabling wider application and accessibility.
- · AI model developers
- · Cloud computing providers
- · Edge AI hardware manufacturers
- · Organizations deploying LLMs
- · Inefficient LLM architectures
More cost-effective deployment and operation of large language models across various industries.
Increased accessibility of advanced AI capabilities due to reduced resource requirements, fostering new applications and innovations.
Accelerated development of even larger and more complex AI models, as compression techniques mitigate the scaling challenges.
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Read at arXiv cs.AI