PrismML ships Bonsai 2 27B: ternary weights near-lossless to the teacher

PrismML

Models / LLM official + media 2 src. ~1 min

PrismML released Bonsai 2 27B (Sep 17), a ternary {-1,0,+1}-weight multimodal model — reasoning, coding, vision, agentic — built on Qwen3.8 27B, retaining 98.2% of the teacher's aggregate benchmarks (83.9 vs 85.4) at a 5.9GB footprint with a 262K context, under Apache 2.0. Reported throughput is up to 143 tok/s on an RTX 5090 and 46.8 tok/s on an M5 Max.

Why it matters

98% benchmark retention at ~5.9GB materially raises the local-agent ceiling — it puts 27B-class coding and vision capability on a single consumer GPU.

Importance: 3/5

Notable open-source model release, 2 confirmations

quantization open-source local-inference moe

Sources

official Bonsai 2 27B: Near-Lossless Compression in a 9x Smaller Footprint

media HN: Bonsai 2 27B (301 points, Sep 17)