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, against 53.80 GB in FP16. PrismML reports that it keeps 98.2% of the parent model’s average across 20 benchmarks. The model accepts text and images and supports a 262K-token context. PrismML demos it driving Cline coding agents and computer use on an RTX 5090. It arrives 2 months after the first Bonsai 27B, whose ternary variant retained about 95%. Is it deployable?
PrismML released a compressed version of a large language model that reduces file size from 53.80 GB to 5.93 GB while retaining 98.2% of the original model's performance across 20 benchmarks. The model uses a ternary-weight format where each weight takes one of three values (negative one, zero, or positive one) instead of full-precision numbers, allowing it to run on standard laptops with 16 GB of memory or single GPUs with 24 GB. Performance losses are uneven across different tasks, with long-horizon agent work dropping to about 75% of the original model's capability, though speed improvements reach 142.5 tokens per second on high-end hardware. The model requires specialized software to run and cannot be loaded by standard tools, limiting its accessibility compared to conventional alternatives.
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