DuoRate: Two-Rate Per-Expert Precision Allocation for Mixture-of-Experts Serving
cs.LG
Submitted: 2026-10-09
Updated: 2026-10-09
Comments: 29 pages, 13 figures, 12 tables
Project page: https://dropbox.github.io/hqq_blog
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Progressive Mixed-Precision Decoding for Efficient LLM Inference
- MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs
- MoPEQ: Mixture of Mixed Precision Quantized Experts
- ELDR: Expert-Locality-Aware Decode Routing for PD-Disaggregated MoE Serving
- SliceMoE: Bit-Sliced Expert Caching under Miss-Rate Constraints for Efficient MoE Inference
- Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference
- Training Verifiers to Solve Math Word Problems
- GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
- HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
- MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design
- Accuracy is Not All You Need
- Measuring Massive Multitask Language Understanding
- Mixture Compressor for Mixture-of-Experts LLMs Gains More
- MC#: Mixture Compressor for Mixture-of-Experts Large Models
- SqueezeLLM: Dense-and-Sparse Quantization
- From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill
- Mix-Quant: Quantized Prefilling, Precise Decoding for Agentic LLMs
- Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs
- HiFloat4 Format for Language Model Inference
- Pointer Sentinel Mixture Models
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