Towards Understanding Best Practices for Quantization of Vision-Language Models

• URL: http://arxiv.org/abs/2601.15287v1
• Date: Wed, 21 Jan 2026 18:59:51 GMT
• Title: Towards Understanding Best Practices for Quantization of Vision-Language Models
• Authors: Gautom Das, Vincent La, Ethan Lau, Abhinav Shrivastava, Matthew Gwilliam,
• Abstract summary: Large language models (LLMs) deliver impressive results for a variety of tasks.<n>To reduce both the memory and latency of these systems, practitioners quantize their learned parameters, typically at half precision.<n>We investigate how a variety of quantization methods, including state-of-the-art GPTQ and AWQ, can be applied effectively to multimodal pipelines.
• Score: 42.75375241956508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) deliver impressive results for a variety of tasks, but state-of-the-art systems require fast GPUs with large amounts of memory. To reduce both the memory and latency of these systems, practitioners quantize their learned parameters, typically at half precision. A growing body of research focuses on preserving the model performance with more aggressive bit widths, and some work has been done to apply these strategies to other models, like vision transformers. In our study we investigate how a variety of quantization methods, including state-of-the-art GPTQ and AWQ, can be applied effectively to multimodal pipelines comprised of vision models, language models, and their connectors. We address how performance on captioning, retrieval, and question answering can be affected by bit width, quantization method, and which portion of the pipeline the quantization is used for. Results reveal that ViT and LLM exhibit comparable importance in model performance, despite significant differences in parameter size, and that lower-bit quantization of the LLM achieves high accuracy at reduced bits per weight (bpw). These findings provide practical insights for efficient deployment of MLLMs and highlight the value of exploration for understanding component sensitivities in multimodal models. Our code is available at https://github.com/gautomdas/mmq.

Related papers

• ShishuLM: Lightweight Language Model with Hybrid Decoder-MLP Architecture and Paired Weight Sharing [0.5565728870245015]
  We introduce an efficient language model architecture, referred to as ShishuLM, which reduces both the parameter count and Key-Value (KV) cache requirements.<n>Our results show that ShishuLM provides up to 25% reduction in memory requirements and up to 40% improvement in latency during both training and inference, compared to parent models.
  arXiv  Detail & Related papers  (2025-10-13T04:04:54Z)
• Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs [78.09559830840595]
  We present the first systematic study on quantizing diffusion-based language models.<n>We identify the presence of activation outliers, characterized by abnormally large activation values.<n>We implement state-of-the-art PTQ methods and conduct a comprehensive evaluation.
  arXiv  Detail & Related papers  (2025-08-20T17:59:51Z)
• Selective State Space Memory for Large Vision-Language Models [0.0]
  State Space Memory Integration (SSMI) is a novel approach for efficient fine-tuning of LVLMs.<n>SSMI captures long-range dependencies and injects task-specific visual and sequential patterns effectively.<n> experiments on benchmark datasets, including COCO Captioning, VQA, and Flickr30k, demonstrate that SSMI achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-12-13T05:40:50Z)
• ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning [38.26304604660713]
  ADEM-VL is an efficient vision-language method that tunes models based on pretrained large language models. Our framework surpasses existing methods by an average accuracy of 0.77% on ScienceQA dataset.
  arXiv  Detail & Related papers  (2024-10-23T11:31:06Z)
• EMMA: Efficient Visual Alignment in Multi-Modal LLMs [56.03417732498859]
  EMMA is a lightweight cross-modality module designed to efficiently fuse visual and textual encodings.<n>EMMA boosts performance across multiple tasks by up to 9.3% while significantly improving robustness against hallucinations.
  arXiv  Detail & Related papers  (2024-10-02T23:00:31Z)
• LLAVADI: What Matters For Multimodal Large Language Models Distillation [77.73964744238519]
  In this work, we do not propose a new efficient model structure or train small-scale MLLMs from scratch. Our studies involve training strategies, model choices, and distillation algorithms in the knowledge distillation process. By evaluating different benchmarks and proper strategy, even a 2.7B small-scale model can perform on par with larger models with 7B or 13B parameters.
  arXiv  Detail & Related papers  (2024-07-28T06:10:47Z)
• MG-LLaVA: Towards Multi-Granularity Visual Instruction Tuning [44.497776004372724]
  Multi-modal large language models (MLLMs) have made significant strides in various visual understanding tasks. We present MG-LLaVA, an innovative MLLM that enhances the model's visual processing capabilities by incorporating a multi-granularity vision flow. To further refine the model's object recognition abilities, we incorporate object-level features derived from bounding boxes identified by offline detectors.
  arXiv  Detail & Related papers  (2024-06-25T17:55:11Z)
• LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit [55.73370804397226]
  Quantization, a key compression technique, can effectively mitigate these demands by compressing and accelerating large language models. We present LLMC, a plug-and-play compression toolkit, to fairly and systematically explore the impact of quantization. Powered by this versatile toolkit, our benchmark covers three key aspects: calibration data, algorithms (three strategies), and data formats.
  arXiv  Detail & Related papers  (2024-05-09T11:49:05Z)
• Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression [64.07696663255155]
  Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these models poses huge challenges for their deployment in real-world applications. We introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT) which effectively transfers the knowledge of LLMs to extremely small-scale models.
  arXiv  Detail & Related papers  (2023-10-24T07:58:20Z)
• Graph Neural Prompting with Large Language Models [32.97391910476073]
  Graph Neural Prompting (GNP) is a novel plug-and-play method to assist pre-trained language models in learning beneficial knowledge from knowledge graphs. Extensive experiments on multiple datasets demonstrate the superiority of GNP on both commonsense and biomedical reasoning tasks.
  arXiv  Detail & Related papers  (2023-09-27T06:33:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.