A Queueing-Theoretic Framework for Stability Analysis of LLM Inference with KV Cache Memory Constraints
📰 ArXiv cs.AI
Learn to analyze LLM inference stability with a queueing-theoretic framework considering KV cache memory constraints
Action Steps
- Apply queueing theory to model LLM inference workflows
- Analyze computation and GPU memory constraints using the proposed framework
- Configure KV cache memory allocation to optimize decoding acceleration
- Test the stability of LLM inference under various memory constraints
- Compare the performance of different cache allocation strategies
Who Needs to Know This
Machine learning engineers and researchers working on large language models can benefit from this framework to optimize inference efficiency and stability
Key Insight
💡 Queueing-theoretic framework can help optimize LLM inference efficiency and stability by considering both computation and GPU memory constraints
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🤖 Analyze LLM inference stability with queueing theory! 📊
Key Takeaways
Learn to analyze LLM inference stability with a queueing-theoretic framework considering KV cache memory constraints
Full Article
Title: A Queueing-Theoretic Framework for Stability Analysis of LLM Inference with KV Cache Memory Constraints
Abstract:
arXiv:2605.04595v1 Announce Type: cross Abstract: The rapid adoption of large language models (LLMs) has created significant challenges for efficient inference at scale. Unlike traditional workloads, LLM inference is constrained by both computation and the memory overhead of key-value (KV) caching, which accelerates decoding but quickly exhausts GPU memory. In this paper, we introduce the first queueing-theoretic framework that explicitly incorporates both computation and GPU memory constraints
Abstract:
arXiv:2605.04595v1 Announce Type: cross Abstract: The rapid adoption of large language models (LLMs) has created significant challenges for efficient inference at scale. Unlike traditional workloads, LLM inference is constrained by both computation and the memory overhead of key-value (KV) caching, which accelerates decoding but quickly exhausts GPU memory. In this paper, we introduce the first queueing-theoretic framework that explicitly incorporates both computation and GPU memory constraints
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