Two 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, it brings stronger reasoning, coding, vision, and agentic capability while preserving 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. 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, it 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.

Across a benchmark suite spanning reasoning, math, coding, instruction following, vision, and agentic tool use, Ternary Bonsai 2 27B scores 83.9, retaining 98.2% of Qwen3.8 27B’s aggregate performance. The key result is not only the aggregate score, but where the capability is retained. Coding agents, tool-use systems, multimodal workflows, and long-horizon tasks are particularly sensitive to model degradation because small errors can compound over many steps. Bonsai 2 27B preserves much of the full-precision model’s performance in exactly these areas while operating at a fraction of the memory footprint.

Ternary Bonsai 2 27B reaches up to 143 tokens/second on NVIDIA Ge Force RTX 5090 and 46.8 tokens/second on M5 Max. On an RTX 4090, it consumes just 0.714 m Wh/token, making it 40% more energy-efficient than an 8B model running in full-precision.