MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs
#MoBiQuant #Mixture-of-Bits Quantization #Elastic LLMs #Token-Adaptive #Quantization Precision #Post-Training Quantization #Large Language Models #Computational Efficiency
📌 Key Takeaways
- Researchers developed MoBiQuant, a new quantization framework for elastic LLM deployment
- The framework addresses challenges with precision-dependent calibration parameters
- MoBiQuant uses recursive residual quantization and token-aware routing
- It enables smooth precision switching without repeated calibration
- The method matches performance of bit-specific calibrated models on LLaMA3-8B
📖 Full Retelling
A team of researchers led by Dongwei Wang published a groundbreaking paper titled 'MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs' on February 21, 2026, introducing a novel quantization framework designed to address challenges with elastic deployment of large language models across different computational environments. The paper presents a solution to the growing need for AI systems that can adapt their computational requirements based on available resources, particularly as large language models become increasingly resource-intensive. The researchers identified that traditional quantization methods struggle with elastic deployment because calibration parameters are typically tied to specific precision levels, creating difficulties when switching between different precision levels during runtime. They attribute this problem to varying token-level sensitivity caused by precision-dependent outlier migration during quantization processes. To overcome these limitations, the team developed MoBiQuant, which introduces two innovative components: many-in-one recursive residual quantization that can iteratively reconstruct higher-precision weights, and a token-aware router that dynamically selects the number of residual bit slices based on the sensitivity of individual tokens. Experimental results demonstrate that MoBiQuant exhibits strong elasticity, enabling it to match the performance of bit-specific calibrated post-training quantization on the LLaMA3-8B model without requiring repeated calibration, which significantly reduces computational overhead and makes elastic deployment more practical for real-world applications.
🏷️ Themes
Machine Learning, Quantization Optimization, Computational Efficiency
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Large language model:
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Educational technology
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Reinforcement learning
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Machine learning
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Artificial intelligence
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Benchmark
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Original Source
--> Computer Science > Machine Learning arXiv:2602.20191 [Submitted on 21 Feb 2026] Title: MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs Authors: Dongwei Wang , Jinhee Kim , Seokho Han , Denis Gudovskiy , Yohei Nakata , Tomoyuki Okuno , KhayTze Peong , Kang Eun Jeon , Jong Hwan Ko , Yiran Chen , Huanrui Yang View a PDF of the paper titled MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs, by Dongwei Wang and 10 other authors View PDF HTML Abstract: Changing runtime complexity on cloud and edge devices necessitates elastic large language model deployment, where an LLM can be inferred with various quantization precisions based on available computational resources. However, it has been observed that the calibration parameters for quantization are typically linked to specific precisions, which presents challenges during elastic-precision calibration and precision switching at runtime. In this work, we attribute the source of varying calibration parameters to the varying token-level sensitivity caused by a precision-dependent outlier migration this http URL by this observation, we propose \texttt , a novel Mixture-of-Bits quantization framework that adjusts weight precision for elastic LLM inference based on token sensitivity. Specifically, we propose the many-in-one recursive residual quantization that can iteratively reconstruct higher-precision weights and the token-aware router to dynamically select the number of residual bit slices. MoBiQuant enables smooth precision switching while improving generalization for the distribution of token outliers. Experimental results demonstrate that MoBiQuant exhibits strong elasticity, enabling it to match the performance of bit-specific calibrated PTQ on LLaMA3-8B without repeated calibration. Comments: 17 pages, 12 figures Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2602.20191 [cs.LG] (or arXiv:2602.20191v1 [...
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