GoKawiilTwo months ago, we released our first Bonsai 27B models and showed that a 27B-class multimodal model could be compressed enough to run efficiently on a local device. Today, we’re releasing Ternary Bonsai 2 27B, our most capable model yet.
Based on Qwen3.8 27B, Ternary Bonsai 2 27B brings stronger reasoning, coding, vision, and agentic capability to the Bonsai series while preserving the deployment profile that defines it: a dramatically smaller memory footprint, high local throughput, and better energy efficiency.
Ternary Bonsai 2 27B uses ternary {−1, 0, +1} weights with FP16 group-wise scaling, for 1.76 effective bits per weight and a total model footprint of 5.9GB. The low-bit representation is applied end to end across the language model. It supports a 262K-token context window, multimodal text-and-image input, and is released under the Apache 2.0 license.
Against its full-precision counterpart, Ternary Bonsai 2 27B is more than 9x smaller while retaining 98.2% of aggregate benchmark performance. At this level of retention, compression becomes a deployment unlock: nearly the same capability, in a footprint that can run in far more places.
What changed from the first Bonsai 27B release
Our first Bonsai 27B release was an important milestone, offering a practical way to run 27B-class intelligence on local devices. Bonsai 2 27B focuses on the next step: improving the model quality and runtime performance needed for real-world local applications. Compared with the previous Bonsai 27B generation, Bonsai 2 27B brings:
a stronger base model, Qwen3.8 27B
higher aggregate capability retention of 98.2% against the full-precision model
improved reasoning, coding, vision, and long-horizon agentic performance
Higher capability at the same deployment point
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