大模型2-3比特量化精度崩坏？这项研究用分组离散优化突破极限，无需反向传播即可部署。
arXiv:2608.15567v1 Announce Type: new Abstract: Weight-only post-training quantization (PTQ) enables the deployment of large language models under tig…
1.58比特量化新框架ScaleQ-1.58，专治推理大模型量化掉点，用三元PTQ保住思维链能力，硬核干货。
arXiv:2608.01078v1 Announce Type: cross Abstract: We propose ScaleQ-1.58, a scalable ternary post-training quantization (PTQ) framework for reasoning …
提出GPTQ-2D算法，实现三次时间复杂度的双向自适应舍入，有效提升大模型量化精度。
arXiv:2607.27042v1 Announce Type: cross Abstract: Adaptive rounding methods such as GPTQ, or equivalently Babai's nearest plane algorithm, round a rea…
新方法Fair-GPTQ实现大模型量化时兼顾效率与公平性，减少偏差
arXiv:2509.15206v3 Announce Type: replace Abstract: The high memory demands of generative language models have drawn attention to quantization, which …
多模态大模型也能压到超低比特？LUQ提出逐层量化新方案，让部署更省内存算力。
arXiv:2509.23729v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) with multimodal capabilities have revolutionized vision-languag…
量化检查点常先用质量指标筛选，但论文揭示36组质量-安全配对均不一致，安全检测不可跳过
arXiv:2606.10154v1 Announce Type: new Abstract: Quantized checkpoints are often screened first with quality metrics and only later, if at all, with di…
针对扩散大语言模型量化中的“稳定性滞后”问题，提出前沿感知重加权校准方法，有效抑制量化误差导致的早期决策翻转。
arXiv:2606.06547v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) refine tokens iteratively but commit them irreversibly, lead…
一文讲清GGUF、GPTQ、AWQ三种主流LLM量化格式的区别，帮你选对压缩方式省显存。
Article URL: https://vettedconsumer.com/gguf-vs-gptq-vs-awq-the-plain-english-guide-to-llm-quantization-and-which-one-to-pick/ Comments URL: https://n…
提出时间步感知的W4A4量化方案，攻克视频扩散Transformer的激活异常与分布依赖难题，内存省出新高。
arXiv:2605.27003v1 Announce Type: cross Abstract: W4A4 quantization of large video diffusion Transformers offers substantial memory savings but is hin…