MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Acceleration
cs.AR, cs.CV
Submitted: 2026-09-17
Updated: 2026-10-08
Comments: Accepted to the 59th IEEE/ACM International Symposium on Microarchitecture (MICRO 2026)
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
- Rethinking Practical and Efficient Quantization Calibration for Vision-Language Models
- VEQ: Modality-Adaptive Quantization for MoE Vision-Language Models
- P4Q: Learning to Prompt for Quantization in Visual-language Models
- Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
- LLaVA-OneVision: Easy Visual Task Transfer
- MiniCPM-V: A GPT-4V Level MLLM on Your Phone
- Towards VQA Models That Can Read
- ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
- VizWiz Grand Challenge: Answering Visual Questions from Blind People
- SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
- LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
- EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
- MicroMix: Efficient Mixed-Precision Quantization with Microscaling Formats for Large Language Models
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