Argus: Token-Aware LLM Inference Optimization
Published:Dec 28, 2025 13:38
•1 min read
•ArXiv
Analysis
This paper addresses the critical challenge of optimizing LLM inference in dynamic and heterogeneous edge-cloud environments. The core contribution lies in its token-aware approach, which considers the variability in output token lengths and device capabilities. The Length-Aware Semantics (LAS) module and Lyapunov-guided Offloading Optimization (LOO) module, along with the Iterative Offloading Algorithm with Damping and Congestion Control (IODCC), represent a novel and comprehensive solution to improve efficiency and Quality-of-Experience in LLM inference. The focus on dynamic environments and heterogeneous systems is particularly relevant given the increasing deployment of LLMs in real-world applications.
Key Takeaways
- •Argus is a token-aware framework for distributed LLM inference.
- •It addresses the variability in inference time caused by autoregressive architectures.
- •Key components include LAS for token length prediction and LOO for offloading optimization.
- •IODCC is used to solve the optimization problem under time-varying constraints.
- •The framework is designed for dynamic and heterogeneous edge-cloud environments.
Reference
“Argus features a Length-Aware Semantics (LAS) module, which predicts output token lengths for incoming prompts...enabling precise estimation.”