Multi-Objective Structured Pruning of LLMs for Latency and Model Size Optimization
Quick Answer
The proposed multi-objective structured pruning framework optimizes large language models (LLMs) for edge deployment by reducing latency and model size while maintaining performance.
Quick Take
This two-stage method achieves a favorable trade-off, demonstrating improved commonsense reasoning task performance at 37.5% and 50% pruning ratios compared to existing techniques, significantly lowering inference costs.
Key Points
- Introduces a hardware-aware pruning framework targeting latency and model size.
- Employs coarse-grained depth pruning to eliminate entire attention and MLP blocks.
- Utilizes Parallel Bayesian Optimization for optimal layer-wise pruning ratios.
- Achieves better performance on commonsense reasoning tasks than existing methods.
- Reduces inference costs significantly while maintaining model performance.
DeepSignal Analysis
What happened
A new multi-objective structured pruning framework for large language models (LLMs) has been proposed to optimize them for edge deployment. This framework aims to reduce both latency and model size while maintaining performance levels. The method includes a two-stage approach that effectively prunes model components to achieve these goals.
Key evidence
- The proposed framework employs a two-stage method that includes coarse-grained depth pruning and fine-grained layer-wise pruning to optimize latency and model size.
- Experimental results indicate that the method achieves better performance on commonsense reasoning tasks at 37.5% and 50% pruning ratios compared to existing techniques.
- The approach significantly reduces inference costs while maintaining minimal impact on model performance, making it suitable for deployment in resource-constrained environments.
Why it matters
Optimizing LLMs for edge deployment is crucial due to the increasing demand for efficient AI applications in resource-limited settings. By addressing latency and memory constraints, this framework could facilitate broader adoption of LLMs in practical applications. The ability to maintain performance while reducing model complexity is particularly significant for developers and organizations looking to implement AI solutions in real-world scenarios.
Paper Resources
Source Excerpt
(LLMs) have achieved widespread adoption because of their strong reasoning and query-response capabilities. However, deploying them in embedded and edge computing environments remains challenging because of strict latency, memory, and energy constraints. Their large parameter counts and computational demands hinder efficient execution on resource-constrained platforms. Although model pruning has emerged as a viable solution for reducing scale while preserving performance, j
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
AINTMA, an autonomous test management architecture utilizing six specialized AI agents, achieves 88.4% test prioritization accuracy and reduces defect escape rates from 8.3% to 2.1%. The system demonstrates a 340% ROI within nine months, showcasing the potential of agentic AI in enhancing software quality management in cloud environments.