FP16 group-wise scaling
AssessTechniques
A scaling method that helps preserve accuracy when using low-bit weights.
Why it's here
Placed in Assess: 1 article(s) of evidence from 1 source(s), led by model releases, with 1 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.
Evidence (1)
- 7Hacker News·9/17/2026model_releasePrismML launches Bonsai 2 27B with 9x smaller footprint
PrismML released Ternary Bonsai 2 27B, a compressed multimodal model based on Qwen3.8 27B. The company says it retains 98.2% of full-precision benchmark performance while using ternary weights and a 5.9GB footprint, targeting local deployment for coding, vision, and agentic workloads.