# MoE-Beyond: Learning-Based Expert Activation Prediction on Edge Devices

arXiv: https://arxiv.org/abs/2508.17137 · HTML: https://mlsanity.com/paper/2508.17137/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Efficient MoE training and inference systems, one of 12 directions in Mixture-of-experts.
- Builds on 0 of its 6 references that are in the map.
- First posted on arXiv 23 Aug 2025.

## Placed in

- Mixture-of-experts / [Efficient MoE training and inference systems](https://mlsanity.com/moe/efficient-moe-training-and-inference-systems/)

## Built on by (most cited first, 3 of 3)

- [DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurance](https://mlsanity.com/paper/2509.07379/) (2025)
- [SpecPrefetch: Parameter-Efficient Expert Prefetching for Sparse MoE Foundation Models](https://mlsanity.com/paper/2607.24787/) (2026)
- [Reproducible Evaluation of MoE Expert Caching: Replay Semantics, Workload Contamination, and Operating Regimes](https://mlsanity.com/paper/2608.07911/) (2026)

## Abstract (arXiv)

The deployment of large-scale Mixture-of-Experts (MoE) models on edge devices presents significant challenges due to memory constraints. While MoE architectures enable efficient utilization of computational resources by activating only a subset of experts per inference, they require careful memory management to operate efficiently in resource-constrained environments. Traditional heuristic-based expert caching strategies such as MoE-Infinity struggle to maintain high cache hit rates as models parameters scale. In this work, we introduce MoE-Beyond, a learning-based expert activation predictor trained to predict expert activations during autoregressive decoding. By framing the task as a multi-label sequence prediction problem, we train a lightweight transformer model on 66 million expert activation traces extracted from LDJnr-Puffin dataset [5] using DeepSeek-V2-Chat-Lite MoE. Our predictor generalizes effectively across unseen prompts from WebGLM-QA dataset [6], achieving 97.5% accuracy and an 86.6% F1-score. Simulation results show that MoE-Beyond improves GPU cache hit rate from 17% to 72% when only 10% of experts fit in GPU cache, outperforming heuristic baselines.