Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging
Paper recorded by Signals 4 on 2026-09-29 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-29 on arXiv · recorded by Signals 4 on 2026-09-30
Category: cs.LG · 机器学习 · first seen 2026-09-30
Abstract
Mixture-of-Experts (MoE) models are a compelling architecture for scaling model capacity, making them especially attractive for deployment on resource-constrained, single-GPU systems. However, this benefit is difficult to realize because expert parameters dominate memory, and token-level routing is dynamic, unpredictable, and skewed. Prior work using offloading and caching remains fundamentally re
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Cite this page: Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging: the #12 most recent of 334 cs.LG papers we have recorded (as of 2026-09-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/mira-memory-efficient-moe-inference-using-adaptive-caching-and-predictive-expert.html
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