DyMoE: Dynamic Expert Orchestration with Mixed-Precision Quantization for Efficient MoE Inference on Edge

Documenti analoghi

• Fate: Fast Edge Inference of Mixture-of-Experts Models via Cross-Layer Gate
  di: Fang, Zhiyuan, et al.
  Pubblicazione: (2025)
• Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch Pipeline
  di: Fang, Zhiyuan, et al.
  Pubblicazione: (2025)
• OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
  di: Shi, Jingze, et al.
  Pubblicazione: (2026)
• GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
  di: Deng, Jianing, et al.
  Pubblicazione: (2026)
• OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference
  di: Wang, Liujianfu, et al.
  Pubblicazione: (2025)