arXiv:2605.10933v3 Announce Type: replace Abstract: While Mixture-of-Experts (MoE) scales model capacity without proportionally increasing computation, its massive total parameter footprint creates significant storage and memory-access bottlenecks, which hinder efficient end-side deployment that simultaneously requires high performance, low computational cost, and small storage overhead. To achieve these properties, we present DECO, a sparse MoE architecture designed to match the performance of dense Transformers under identical total parameter budgets and training tokens. DECO utilizes the di
The rapid advancement of AI models necessitates solutions for deploying increasingly complex architectures efficiently on resource-constrained 'end-side' devices.
This development addresses a critical bottleneck in AI scaling—efficient deployment of powerful models on edge devices, unlocking new applications and broader access to advanced AI capabilities beyond large data centers.
The ability to deploy sparse Mixture-of-Experts (MoE) models with dense-comparable performance on end-side devices significantly reduces the computational and storage overhead previously associated with high-capacity AI models at the edge.
- · Edge AI hardware manufacturers
- · On-device AI application developers
- · Consumer electronics industry
- · Developers of AI agents
- · Providers of cloud-only AI inference for some tasks
- · Companies reliant solely on massive data centers for inferencing
Widespread adoption of more sophisticated AI models on local devices without constant cloud connectivity.
Increased privacy and reduced latency for many AI applications as data processing shifts from cloud to device.
Acceleration of autonomous AI agents operating in real-world, dynamic environments with greater efficiency and robustness.
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Read at arXiv cs.LG