Was BitNet a dead end? What happened to ternary LLMs?
English summary
This Reddit post questions why ternary LLMs like BitNet have not scaled beyond 2B parameters despite initial promise. The author wonders why frontier open-weight AI labs have not adopted ternary approaches. Comments may discuss technical limitations or lack of practical benefits. The post reflects community curiosity about the viability of ternary architectures for large-scale models.
Chinese summary
这篇 Reddit 帖子质疑为什么像 BitNet 这样的三元大语言模型尽管最初很有前景，但规模未能超过 20 亿参数。作者想知道为什么前沿的开源权重 AI 实验室没有采用三元方法。评论可能讨论技术限制或缺乏实际收益。该帖子反映了社区对三元架构在大规模模型中可行性的好奇。
Key points
Ternary LLMs (e.g., BitNet) have not progressed beyond 2B parameters.
三元大语言模型（如 BitNet）尚未突破 20 亿参数规模。
Community questions why frontier open-weight labs are not exploring ternary architectures.
社区质疑为什么前沿开源权重实验室没有探索三元架构。
Potential reasons include technical hurdles or limited performance gains.
潜在原因包括技术障碍或性能提升有限。