The increasing deployment of Large Language Model (LLM) inference on edge AI systems demands efficient execution under tight memory budgets. A key challenge arises from Key-Value (KV) caches, which often exceed available device memory. Although NVMe-based offloading offers scalable capacity, existing file-based designs rely heavily on the kernel page cache, leading to cache thrashing, unpredictable latency, and high software overhead under memory pressure. We present DUAL-BLADE, a dual-path KV residency framework that dynamically assigns KV tensors to either a page-cache path or an NVMe-direct path based on runtime memory availability. The NVMe-direct path bypasses the filesystem by mapping KV tensors to contiguous logical block address (LBA) regions, enabling low-overhead direct storage access. DUAL-BLADE further incorporates adaptive pipeline parallelism to overlap storage I/O with GPU DMA, improving inference throughput. Our evaluation shows that DUAL-BLADE substantially mitigates I/O bottlenecks, reducing prefill and decode latency by up to 33.1% and 42.4%, respectively, while improving SSD utilization by 2.2x across diverse memory budgets.
@article{arxiv.2604.26557,
title = {DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference},
author = {Bodon Jeong and Hongsu Byun and Youngjae Kim and Weikuan Yu and Kyungkeun Lee and Jihoon Yang and Sungyong Park},
journal= {arXiv preprint arXiv:2604.26557},
year = {2026}
}
Comments
To appear in IEEE International Conference on Distributed Computing Systems (ICDCS) 2026