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arXiv · 2603.10087

Pooling Engram Conditional Memory in Large Language Models using CXL

Ruiyang Ma·Teng Ma·Zhiyuan Su·Hantian Zha·Xinpeng Zhao·Xuchun Shang·Xingrui Yi·Zheng Liu·Zhu Cao·An Wu·Zhichong Dou·Ziqian Liu·Daikang Kuang·Guojie Luo
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Abstract

Engram conditional memory has emerged as a promising component for LLMs by decoupling static knowledge lookup from dynamic computation. Since Engram exhibits sparse access patterns and supports prefetching, its massive embedding tables are well-suited for offloading to lower-tier memory. In this paper, we propose using Compute Express Link (CXL) memory pool for Engram storage. Compared to RDMA, CXL provides fine-grained and low-latency access required by minimal and discrete retrieval patterns of Engram. We integrate the CXL-based Engram pool into SGLang, achieving near-DRAM end-to-end performance. This provides a scalable and cost-efficient storage solution for future Engram-integrated LLMs without compromising inference performance.

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Ruiyang Ma, Teng Ma, Zhiyuan Su, Hantian Zha, Xinpeng Zhao, Xuchun Shang, Xingrui Yi, Zheng Liu, Zhu Cao, An Wu, Zhichong Dou, Ziqian Liu, Daikang Kuang, Guojie Luo. 2026-03-10. Pooling Engram Conditional Memory in Large Language Models using CXL. https://arxiv.org/abs/2603.10087

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