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Public story · 2026-09-18 · high
Prism ML says its 1.72-bit model keeps 262K context and near-FP16 quality, but a same-day failure report says otherwise.
Why now: Prism ML posted the model on September 16, and a counter-report went up the same day.
Prism ML released Ternary Bonsai 2 27B on September 16 as GGUF and MLX builds. Embeddings, attention projections, MLP projections and the LM head all get stored as ternary weights limited to -1, 0, or +1, with FP16 group scaling layered on top.
That takes a roughly 54GB FP16 model down to 5.95GB while keeping 262K context. For anyone running local inference, that's a real jump in the quality-per-gigabyte tradeoff, if the numbers hold.
The model card claims 98.2% of FP16 intelligence retained. It backs that with an 84.78 average across 14 thinking-mode benchmarks, against 72.59 for a conventional IQ2_XXS build. Prism ML also lists about 47 tokens per second on an M5 Max.
Downloads moved fast: 405,609 in two days. A same-day r/LocalLLaMA thread titled "Ternary Bonsai is a headless chicken" reports the model failing a single 3D-scene coding prompt. One failed prompt isn't a benchmark suite. The thread doesn't run Prism ML's 14-test battery, so it doesn't refute the card's numbers directly, but it does leave the 98.2% figure sitting on Prism ML's own testing alone. Nobody outside the lab has published an independent eval yet.
The model card doesn't say which 14 benchmarks made up that average, or whether any overlap with what the community tests on its own.
Each link below shares sources, entities, or timing with this story.
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