Bonsai 2 27B

User benchmarks Ternary Bonsai 2 27B at ~6 t/s generation and ~18 t/s prefill on an NVIDIA T1000 8GB, comparing it against Gemma 4 26B-A4B for audiobook speaker attribution. Setup is a PrismML llama.cpp fork with PTQ1_0 ternary weights (5.95 GB), q8_0 KV cache, 8192 context, and all layers on GPU; the model scores 448/480 (93.3%) exact speaker matches on four chapters. The user does not adopt it, citing narrator identity drift, adjacent-turn swaps, dropped lines, and an intonation regression at 5-10x the wall-clock of the incumbent Gemma 4 26B-A4B MoE.