# Tested Bonsai 2 27B Locally (Ternary Model)

> Source: <https://www.dotnetperls.com/2026_9_18_tested-bonsai-2-27b-locally>
> Published: 2026-09-18 07:00:00+00:00

Ternary models represent each weight as one of **3 values** (1, 0, and -1). They can be much **smaller** and **faster** than other quantizations but this comes at a price of quality. I downloaded the **Bonsai 2** model (which is 7.2 GB) and used it in Pi code.

First, a special version of `llama-cpp` is currently needed and can be downloaded from PrismML's GitHub. I have a 12GB Nvidia GPU so the 7.2 GB model **fits nicely**; the 4-bit quantization of Qwen 3.8 would not fit. Bonsai 2 is based on Qwen 3.8 27B, a popular local coding model.

I found:

• Bonsai 2 27B works as an agentic coder, and it rarely fails tool calls, although it tends to over-think like the original Qwen 3.8 model does.

• Bonsai 2 27B is a significant improvement over the previous Bonsai 27B models in quality of output.

• The claim that Bonsai 2 27B is only 2% degraded from Qwen 3.8 27B may be at least somewhat accurate in general.

• On an RTX 3060 desktop card with 12 GB RAM the model decodes at around 30 tokens per second, which is usable (although the fan is loud).

• Bonsai 2 is useful for real coding tasks and may help alleviate the need to buy GPUs with huge amounts of memory.