Bonsai 2 27B: Near-Lossless Compression in a 9x Smaller Footprint

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, 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. 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: 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. Compared with the full-precision model and other low-bit alternatives, Bonsai 2 27B stands out as an outlier on intelligence density. Many low-bit alternatives become deployable only , vision, or agentic tool use. Bonsai 2 27B pushes the frontier toward both higher capability and lower memory usage. With Bonsai 2 27B, local models can start to take on real knowledge work: coding-agent loops, computer-use workflows, private document analysis, multimodal debugging, and hybrid orchestration where local models handle sensitive or high-frequency tasks while escalating selectively to the cloud. Ternary Bonsai 2 27B reaches up to 143 tokens/second on NVIDIA GeForce RTX 5090 and 46.8 tokens/second on M5 Max.