Dynamic Memory Management for Large Language Models
By: Mingxuan Wang, Hongkun Ma, Zifeng Wang, Jianxiong Li, Jun Huang
Published: 2025-12-03
View on arXiv →Abstract
This paper addresses the challenge of efficient memory utilization in Large Language Models through a novel dynamic memory management system. It aims to optimize resource allocation, reduce computational overhead, and enable more scalable and cost-effective deployment of LLMs in diverse real-world applications.
Impact
practical
Topics
6
💡 Simple Explanation
Imagine a library where you must reserve a whole shelf for every person who walks in, assuming they might read 100 books, even if they only read one. The shelves fill up quickly, and new people are turned away. This paper introduces a system like a librarian who hands out one book slot at a time, anywhere in the library, tracking exactly where everyone's books are. This way, the library can serve many more people at once without wasting empty shelf space.
🎯 Problem Statement
LLM inference suffers from memory fragmentation because the length of the output sequence is unknown beforehand. Standard frameworks reserve the maximum possible memory, leading to 'internal fragmentation' where expensive GPU memory sits reserved but unused, drastically limiting the number of concurrent requests (batch size) a GPU can handle.
🔬 Methodology
The authors propose a Paged Attention mechanism. Instead of allocating contiguous memory for the Key and Value matrices of the attention mechanism, they divide the KV cache into fixed-size blocks. A block table manages the mapping between logical tokens (the sentence structure) and physical blocks (locations in GPU memory). The attention kernel is rewritten to fetch data via these block tables, enabling dynamic allocation.
📊 Results
The proposed method achieves near-zero memory waste (<4% compared to 60-80% in standard systems). This efficiency allows for a 2x-4x increase in batch size on the same hardware. Consequently, the serving throughput increases by significantly without degrading model accuracy. The system supports decoding with very long sequences that would previously trigger Out-Of-Memory (OOM) errors.
✨ Key Takeaways
Memory management is the key to economic LLM deployment. By borrowing decades-old ideas from Operating Systems (paging), we can solve modern AI bottlenecks. This approach transforms the GPU memory from a rigid container into a flexible resource pool.
🔍 Critical Analysis
This work represents a foundational shift in how we engineer LLM inference systems. By treating GPU memory management as an OS problem rather than a static tensor allocation problem, it unlocks massive efficiency gains. However, it increases the engineering complexity significantly, moving the burden from the framework (PyTorch) to the serving engine developer. The reliance on custom kernels creates a maintenance debt.
💰 Practical Applications
- Cost-saving plugin for Kubernetes GPU clusters.
- High-performance proprietary inference API.
- Licensing the memory management IP to chip manufacturers.