arXiv AI
Jul 1

KV-RM: Regularizing KV-Cache Movement for Static-Graph LLM Serving

arXiv:2605. 09735v2 Announce Type: replace-cross Abstract: Static-graph LLM decoders provide predictable launches, fixed tensor shapes, and low submission overhead, but online decoding exposes highly irregular KV-cache behavior: request lengths differ, EOS events arrive asynchronously, and logical histories fragment over time.

By Zhiqing Zhong, Zhijing Ye, Jian Zhang, Weijian Zheng, Bolun Sun, Xiaodong Yu
arXiv Machine Learning
Sep 25

Paging the Experts: A Reproducible Characterization of Flash-Backed MoE Inference on iPhone

The paper introduces Routide, a Swift/MLX runtime that runs a quantized Qwen3.6-35B-A3B model on iPhone by keeping expert weights on device storage and a byte‑budgeted subset in memory. It evaluates cache‑policy effects, showing that a 512 MiB LRU cache yields 0.00% demand hits while a 576 MiB LRU reaches 38.58% hits across five 128‑token workloads, indicating that capacity limits depend on policy and workload. The study also reports memory footprints, thermal events, and power estimates, demonstrating that flash‑backed MoE inference is feasible within bounded resources but has measurable limitations.

By Musa Shams
arXiv Machine Learning
Sep 7

KVMem: Virtualizing Million-Token Agent Workspaces on a Consumer GPU

KVMem is a KV-context virtualization system that allows large language model agents to maintain workspaces exceeding both GPU key‑value capacity and the model’s native context window. It stores overflowed history as paged KV state across GPU memory, host memory, and NVMe, using lightweight, model‑native attention‑space indexes to retrieve relevant historical blocks. Evaluations on long‑context agent benchmarks show that KVMem improves task utility and inference efficiency, enabling up to one million‑token workspaces on consumer GPUs and achieving interactive responsiveness in local deployments.

By Di Chai, Leye Wang, Zeshen Su, Zhiguo Xia, Zhihang Yu