aiqbench analysis

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.