Was BitNet a dead end? What happened to ternary LLMs?
A LocalLLaMA post asks why ternary LLMs have not scaled beyond small models.
A r/LocalLLaMA user questions whether BitNet and ternary LLMs were a dead end after earlier promise around efficient low-bit models. The post notes that the largest ternary model appears to remain around 2B parameters. It asks why frontier open-weight AI labs are not visibly pursuing the approach, but provides no technical evidence or definitive answer.
這篇來自 r/LocalLLaMA 的貼文不是一篇正式研究或產品發布，而是一個針對 BitNet 與 ternary LLM 發展狀況的社群提問。發文者指出，三元權重模型曾經看起來相當有前景，因為它們理論上可能用更低的位元表示模型權重，進而降低記憶體占用與推論成本，對本地端 LLM、低成本部署與硬體效率都有吸引力。不過，發文者觀察到目前最大的 ternary model 似乎仍停留在 2B 參數等級，並未看到明顯向 7B、13B 或更大開放權重模型擴張的趨勢。
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