NeuroPrefetcher: Storage-Aware Sparse LLM Inference via Delta Prefetching
Deploying large language models on edge devices is increasingly limited by a widening gap between model size and available memory. Existing approaches such as quantization, smaller models, and offloading can raise the effective memory limit, but they still assume that the model can be compressed or partitioned to fit within some budget. We target the harder model-exceeds-memory setting, in which the model remains larger than resident memory throughout execution and storage becomes an active source of weights on the critical path. We observe that MLP activity during autoregressive decoding has strong temporal locality: approximately 82-85% of active neurons persist from one token to the ne...
| Authors | Nobel Dhar, Md Romyull Islam, Xuechen Zhang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.