Optimizing Distributed LLM Inference Resource Allocation
Research Paper#Large Language Models (LLMs), Distributed Systems, Resource Allocation, Inference Optimization🔬 Research|Analyzed: Jan 3, 2026 16:36•
Published: Dec 26, 2025 06:13
•1 min read
•ArXivAnalysis
This paper addresses the critical problem of optimizing resource allocation for distributed inference of Large Language Models (LLMs). It's significant because LLMs are computationally expensive, and distributing the workload across geographically diverse servers is a promising approach to reduce costs and improve accessibility. The paper provides a systematic study, performance models, optimization algorithms (including a mixed integer linear programming approach), and a CPU-only simulator. This work is important for making LLMs more practical and accessible.
Key Takeaways
- •Addresses the resource allocation problem for distributed LLM inference.
- •Proposes performance models for predicting inference performance.
- •Formulates the optimization problem as mixed integer linear programming.
- •Develops a CPU-only simulator for performance evaluation.
- •Demonstrates improved inference time compared to state-of-the-art solutions.
Reference / Citation
View Original"The paper presents "experimentally validated performance models that can predict the inference performance under given block placement and request routing decisions.""