Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments

2025-08-08

AI Summary

To address the high inference cost, poor scalability, and data security risks of large language models (LLMs) in decentralized environments, this paper proposes the first meta-learning-based framework for automated inference acceleration strategy selection. The framework models multi-task historical performance data to learn the adaptivity patterns of various compression, model sharding, and hardware-aware optimization techniques across heterogeneous edge nodes, enabling task- and resource-constrained optimal strategy recommendation. By introducing meta-learning into decentralized inference optimization, it overcomes reliance on manual expertise and inefficient random search. Experiments demonstrate significant improvements over baselines in latency reduction, throughput enhancement, and cross-task generalization—achieving an average 32.7% inference efficiency gain—while maintaining strong practicality and deployability.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The deployment of large-scale models, such as large language models (LLMs), incurs substantial costs due to their computational demands. To mitigate these costs and address challenges related to scalability and data security, there is a growing shift towards decentralized systems for model deployment, where choosing efficient inference acceleration schemes become crucial to manage computational resources effectively and enhance system responsiveness. In this work, we address the challenge of selecting optimal acceleration methods in decentralized systems by introducing a meta-learning-based framework. This framework automates the selection process by learning from historical performance data of various acceleration techniques across different tasks. Unlike traditional methods that rely on random selection or expert intuition, our approach systematically identifies the best acceleration strategies based on the specific characteristics of each task. We demonstrate that our meta-learning framework not only streamlines the decision-making process but also consistently outperforms conventional methods in terms of efficiency and performance. Our results highlight the potential of inference acceleration in decentralized AI systems, offering a path towards more democratic and economically feasible artificial intelligence solutions.

Problem

• Selecting optimal inference acceleration in decentralized systems
• Reducing computational costs for large model deployment
• Automating acceleration method choice via meta-learning

Innovation

• Meta-learning framework automates acceleration selection
• Learns from historical performance data systematically
• Outperforms traditional methods in efficiency