UkisAI Gauges Community Interest for Swift Ternary Bonsai 2 27B Optimization
UkisAI reached out to the r/LocalLLaMA community to gauge interest in creating a token-efficient, ternary-quantized 'Swifted' version of the Bonsai 2 27B model. The proposed optimization aims to fix overthinking loops and reduce token consumption, following the success of their Swift Qwen model. Addressing overthinking behavior and excessive token usage in reasoning LLMs significantly reduces inference latency and hardware resource demands. Additionally, applying extreme ternary quantization allows capable 27B-parameter models to run efficiently on edge hardware and consumer devices. UkisAI reported that their previous release, Swift Qwen3.8 27B, saw downloads jump from 100,000 to 150,000 overnight. They are actively seeking feedback on whether the local AI community prefers a 1-bit, 2-bit, or dual quantization release for Bonsai 2.
## BACKGROUND
Bonsai 2 27B is a compressed reasoning language model created by PrismML aimed at enabling local AI capabilities on consumer PCs and mobile devices. Ternary quantization is an extreme model compression approach that constrains model weights to just three values (typically -1, 0, and +1), dramatically shrinking memory overhead while preserving reasoning accuracy.