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PrismML's Ternary Bonsai 2 27B demonstrates significant progress in model compression, achieving a 5.9 GB footprint while retaining 98.2% of Qwen3.8's performance through a hybrid ternary-precision architecture. This release highlights the viability of low-bit quantization for deploying large-scale foundation models on consumer hardware without sacrificing critical capabilities like coding and vision tasks. The approach offers a practical pathway for edge deployment, reducing memory requirements by roughly 90% compared to standard FP16 formats while maintaining high-fidelity inference.
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 […] The post PrismML Releases Ternary Bonsai 2 27B: A 5.9 GB Apache 2.0 Model Retaining 98.2% of Qwen3.8 27B Performance appeared first on MarkTechPost.
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? Yes. The…
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