PrismML Releases Ternary Bonsai 2 27B: A 5.9 GB Apache 2.0 Model Retaining 98.2% of Qwen3.8 27B Performance
PrismML has released Ternary Bonsai 2 27B, a ternary-weight version of Qwen3.8 27B. The language model occupies 5.93 GB, compared with 53.80 GB in FP16. PrismML reports that it retains 98.2% of the parent model's average across 20 benchmarks. The model accepts text and images and supports a 262K-token context. PrismML demonstrates it driving Cline coding agents and computer use on an RTX 5090. It arrives two months after the first Bonsai 27B, whose ternary variant retained about 95%.
Is it deployable? Yes. The Apache 2.0 weights run today on a 16 GB laptop or a single 24 GB GPU. You need PrismML's llama.cpp fork or its MLX runtime.
What Is Ternary Bonsai 2 27B?
The model keeps the Qwen3.8 27B architecture unchanged. It has 27.36B parameters, split into a 24.35B language backbone, 2.54B in embeddings and the LM head, and a 0.47B vision tower. The backbone uses hybrid attention, with about 75% linear-attention and 25% full-attention layers.
Ternary weights cover embeddings, attention projections, MLP projections, and the LM head. Only 26.2M parameters, or 0.0976%, remain in higher precision: the recurrent state path and normalization weights. In GGUF format, the vision tower ships separately as a 0.63 GB file, loaded only for image input.
How Does the Ternary Format Work?
Each weight takes one of three values: -1, 0, or +1. Every group of 128 weights shares one FP16 scale. A ternary value carries log2(3), or about 1.585 bits. Adding 16 scale bits per 128 weights gives 1.71 bits per weight. Counting the high-precision tensors brings the model to 1.72 bits per weight.
via MarkTechPost