MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs
arXiv:2602.20191v1 Announce Type: cross Abstract: Changing runtime complexity on cloud and edge devices necessitates elastic large language model (LLM) 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 phenomenon.Motivated by this observation, we propose \texttt{MoBiQuant}, 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 th
arXiv:2602.20191v1 Announce Type: cross Abstract: Changing runtime complexity on cloud and edge devices necessitates elastic large language model (LLM) 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 phenomenon.Motivated by this observation, we propose \texttt{MoBiQuant}, 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.
Executive Summary
The article 'MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs' introduces a novel framework for quantizing large language models (LLMs) to enable elastic inference based on available computational resources. The authors identify the challenge of varying calibration parameters due to token-level sensitivity and precision-dependent outlier migration. They propose MoBiQuant, which uses recursive residual quantization and a token-aware router to dynamically adjust weight precision. The framework aims to improve generalization and enable smooth precision switching without repeated calibration, as demonstrated through experiments on LLaMA3-8B.
Key Points
- ▸ Identification of token-level sensitivity and outlier migration as sources of calibration challenges.
- ▸ Introduction of MoBiQuant framework with recursive residual quantization and token-aware routing.
- ▸ Demonstration of elastic inference capabilities and performance matching bit-specific calibrated PTQ without repeated calibration.
Merits
Innovative Framework
MoBiQuant presents a novel approach to quantization that addresses the dynamic needs of elastic LLM deployment, offering a solution that adapts to varying computational resources.
Improved Generalization
The framework enhances the generalization of token outliers, which is crucial for maintaining performance across different precision levels.
Demerits
Complexity
The recursive residual quantization and token-aware routing mechanisms add complexity to the model, which may require significant computational resources for implementation and fine-tuning.
Limited Scope of Experiments
The experimental results are based on LLaMA3-8B, and the framework's effectiveness across other LLM architectures and datasets remains to be thoroughly validated.
Expert Commentary
The MoBiQuant framework represents a significant advancement in the field of quantization for large language models. By addressing the challenges associated with token-level sensitivity and outlier migration, the authors provide a robust solution for elastic inference. The recursive residual quantization and token-aware routing mechanisms are particularly noteworthy, as they enable dynamic adjustment of weight precision, which is crucial for adapting to varying computational resources. The experimental results on LLaMA3-8B demonstrate the framework's effectiveness in matching the performance of bit-specific calibrated PTQ without the need for repeated calibration. However, the complexity introduced by these mechanisms and the limited scope of the experiments are areas that require further exploration. Future research should focus on validating the framework across a broader range of LLM architectures and datasets to ensure its generalizability. Additionally, the practical implications of MoBiQuant are substantial, as it enhances the deployment flexibility of LLMs on both cloud and edge devices. This can lead to more efficient use of computational resources and reduced calibration efforts, which are critical for large-scale AI applications. From a policy perspective, the framework's success could influence standardization efforts in quantization and elastic model deployment, providing valuable insights for policymakers in resource allocation and infrastructure development.
Recommendations
- ✓ Conduct extensive experiments across various LLM architectures and datasets to validate the generalizability of the MoBiQuant framework.
- ✓ Explore methods to simplify the implementation of recursive residual quantization and token-aware routing to reduce computational overhead.
