PrismML Bonsai 27B Achieves 1-Bit Quantization for Mobile Deployment
July 17, 2026
Bonsai 27B uses binary g128 quantization to compress a Qwen3.6-27B model from 54GB to 3.9GB. This 1.125 bits-per-weight approach maintains 89.5% of FP16 benchmark performance, specifically retaining 91.7% accuracy in mathematical tasks.
HOW THIS AFFECTS YOU
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builderYou can deploy 27B parameter models on mobile hardware with minimal memory overhead.
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researcherThe full-model binary quantization, including embeddings and LM head, provides a new baseline for extreme compression studies.