SlimWise: Decoupling Expert Pruning Across Prefill and Decode for Efficient MoE Serving
cs.LG, cs.AI
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/huggingface/math-verify
Terminology
Sources
- NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
- Retraining-Free Merging of Sparse MoE via Hierarchical Clustering
- ELDR: Expert-Locality-Aware Decode Routing for PD-Disaggregated MoE Serving
- Training Verifiers to Solve Math Word Problems
- Gemma 4 Technical Report
- Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection
- LoRA: Low-Rank Adaptation of Large Language Models
- Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
- Mixtral of Experts
- Pruning and Distilling Mixture-of-Experts into Dense Language Models
- DeepSeek-V3 Technical Report
- Quantized Reasoning Models Think They Need to Think Longer, but They Do Not
- Opportunistic Expert Activation: Batch-Aware Expert Routing for Faster Decode Without Retraining
- SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training
- XShare: Collaborative in-Batch Expert Sharing for Faster MoE Inference
- ExFold: Unified Expert Folding for Training-Free MoE Prefill-Decode Acceleration
- MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
- Qwen3 Technical Report
- Janus: Disaggregating Attention and Experts for Scalable MoE Inference
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