BitsMoE: Cost-Aware Bit Allocation in Spectral Space for MoE LLM Quantization
cs.LG, cs.AI
Submitted: 2026-05-22
Updated: 2026-09-27
Comments: 35 pages, 10 figures, 16 tables. Our code is available at https://github.com/zjiayu064/BitsMoE and our models are available at https://huggingface.co/collections/zjiayu064/bitsmoe
Code: https://github.com/zjiayu064/BitsMoE
Project page: https://dropbox.github.io/hqq_blog
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Qwen Technical Report
- Evaluating Large Language Models Trained on Code
- Training Verifiers to Solve Math Word Problems
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
- Mixtral of Experts
- DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
- Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
- Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
- Unveiling Super Experts in Mixture-of-Experts Large Language Models
- Qwen3 Technical Report
- Qwen2.5-1M Technical Report
- MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition
- HellaSwag: Can a Machine Really Finish Your Sentence?
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