What is it about?
Large language models (LLMs) are powerful but hard to run on phones or edge devices due to memory limits. Our work introduces FlexInfer, a system that runs these models efficiently by deciding what to keep in memory and what to load only when needed, enabling fast and flexible AI on small devices. This helps run big AI models on everyday devices, keeping data private and enabling personalized use without relying on cloud servers.
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Why is it important?
As large language models become more common in daily life, running them directly on personal devices is increasingly important for privacy, speed, and offline access. However, their huge memory demands make this nearly impossible today. Unlike other methods that sacrifice performance or require cloud servers, FlexInfer boosts speed up to 12.5 times while keeping data private and flexibility. This timely innovation supports growing demands for on-device AI, making advanced technology accessible, secure, and customizable for everyone, from healthcare to personal assistants.
Perspectives
As a researcher passionate about making AI accessible, I saw firsthand how memory constraints limit AI on everyday devices, restricting their potential in privacy-sensitive areas like healthcare or education. Developing FlexInfer’s smart memory management felt like unlocking a door to bring powerful language models to everyone’s pocket. Knowing our work could empower secure, on-device AI for millions is incredibly rewarding.
Hongchao Du
City University of Hong Kong
Read the Original
This page is a summary of: FlexInfer: Breaking Memory Constraint via Flexible and Efficient Offloading for On-Device LLM Inference, March 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3721146.3721961.
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