arXiv:2510.05544v2 Announce Type: replace Abstract: Large language models (LLM) and vision-language models (VLM) have achieved state-of-the-art performance, but they impose significant memory and computing challenges in deployment. We present a novel low-rank compression framework to address this challenge. First, we upper bound the change of network loss via layer-wise activation-based compression errors, filling a theoretical gap in the literature. We then formulate low-rank model compression as a bi-objective optimization and prove that a single uniform tolerance yields surrogate Pareto-opt
The proliferation of increasingly larger and more computationally intensive AI models, especially LLMs and VLMs, necessitates urgent solutions for efficient deployment.
This research addresses a critical bottleneck in the practical application and scaling of advanced AI, directly influencing the accessibility and cost-effectiveness of powerful models.
The ability to significantly compress Large Language Models and Vision-Language Models without substantial performance loss changes the economic and technical feasibility of deploying sophisticated AI.
- · AI developers
- · Cloud providers
- · Edge computing
- · AI-powered applications
- · High-latency edge devices
- · Inefficient AI architectures
More powerful AI models become deployable on a wider range of hardware and at lower operational costs.
Increased widespread adoption of advanced AI leads to new applications and services, accelerating AI integration into various sectors.
The competitive landscape shifts towards innovation in efficient AI deployment rather than just model size, potentially democratizing access to cutting-edge AI capabilities.
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