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Show HN: I wrote a 1-bit WebGPU runtime to run a 1.7B LLM in the browser
1-bit量化+WebGPU，让1.7B大模型在浏览器里流畅运行，技术突破值得关注。
Article URL: https://aidekin.com/ Comments URL: https://news.ycombinator.com/item?id=48820583 Points: 4 # Comments: 2
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GSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cache
突破性KV缓存量化方案，实现sub-1-bit压缩，大幅降低推理内存开销却不损精度。
arXiv:2607.01065v1 Announce Type: new Abstract: The deployment of Large Language Models (LLMs) with extended context windows is increasingly constrain…
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Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
颠覆传统，用预训练大模型突破1-bit量化瓶颈，既省存储又保精度。
arXiv:2508.06974v2 Announce Type: replace Abstract: 1-bit LLM quantization offers significant advantages in reducing storage and computational costs. …
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Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models
1-bit量化大模型新思路，输出对齐策略再审视，助力低资源设备高效推理
arXiv:2512.21651v3 Announce Type: replace Abstract: Large Language Models (LLMs) deliver strong performance across a wide range of NLP tasks, but thei…