ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Scheduling
arXiv:2601.21198v2 Announce Type: replace-cross Abstract: While Mixture-of-Experts (MoE) architectures substantially bolster the expressive power of large-language models, their prohibitive memory footprint severely impedes the practical deployment on resource-constrained edge devices, especially when model behavior must be preserved without relying on lossy quantization. In this paper, we present ZipMoE, an efficient and semantically lossless on-device MoE serving system. ZipMoE exploits the synergy between the hardware properties of edge devices and the statistical redundancy inherent to MoE
The proliferation of AI models, especially large language models using MoE architectures, demands increasingly efficient on-device deployment solutions as edge computing capabilities improve.
This development enables the practical deployment of powerful AI models on resource-constrained edge devices, expanding AI's reach beyond cloud infrastructure and making it more accessible and resilient.
MoE architectures can now be served efficiently on edge devices without relying on lossy quantization, preserving model accuracy while reducing memory footprint.
- · Edge device manufacturers
- · AI application developers
- · Consumers of AI services
- · Hardware developers
- · Cloud-centric AI service providers (marginally)
- · Developers reliant on lossy compression methods
More powerful AI models become ubiquitous on mobile phones, wearables, and IoT devices.
Reduced latency and increased privacy for AI applications due to less reliance on cloud processing.
New classes of AI-powered edge applications emerge that were previously impractical due to computational constraints.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG