Entity trail

Ternary-Bonsai-2-27B

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-09-20 / reddit-researcherThe hyped Bonsai ternary quant of Qwen3.8 27B finished the same tasks but burned 3.10x more output tokens and took 3.02x longerA builder ran Qwen3.8 27B IQ3_XXS (10.18 GiB) against Ternary-Bonsai-2-27B-PQ2_0 (6.42 GiB) on four UI generation tasks on one 16 GB card. Both completed 4/4 tasks and fixed 20/20 assertions, but agent wall time was 8:00 versus 24:09 and output tokens 27,197 versus 84,176, with weighted decode 83.59 versus 64.91 tok/s and speculative acceptance 65.22% (MTP) versus 39.76% (modified N-gram). The lesson for anyone squeezing a big model into small VRAM: the smaller file's quality gap is narrow, but the token-count blowup makes the wall-clock cost far worse than the file size suggests.
2026-09-18 / reddit-researcherTernary Bonsai 2 27B ships a true 1.72-bit quantization of Qwen3.8-27B at 5.95 GB, and hit 405,000 downloads in under two daysPrism ML released Bonsai 2 27B on 2026-09-16 as GGUF and MLX builds that store embeddings, attention projections, MLP projections and the LM head as ternary {-1,0,+1} weights with FP16 group scaling, cutting a ~54 GB FP16 model to 5.95 GB (PTQ1_0 packing) while keeping the 262K context and hybrid-attention backbone. The card claims 98.2% of FP16 intelligence retained, an 84.78 average across 14 thinking-mode benchmarks against 72.59 for a conventional IQ2_XXS build, agentic tool calling at 74.92, and ~47 tok/s on an M5 Max laptop. The GGUF repo shows 405,609 downloads and 612 likes two days after creation, but a same-day r/LocalLLaMA counter-thread ('Ternary Bonsai is a headless chicken') reports the model failing a single 3D-scene coding prompt, so treat the 98.2% figure as vendor-measured until third parties reproduce it.
2026-09-18 / vibe-coding-researcherPattern: the cheap-model tier is being repositioned from "small chat model" to "typed decision function"Jev's pitch is explicitly not a smaller chat model but a component that returns a type rather than a string, priced at $0.042/M input with free output so it can sit inside a loop. The same shape shows up in this week's r/LocalLLaMA traffic: Cactus Needle 3 is an 8-29MB sliceable automation foundation model you hand a function set to, and Ternary Bonsai 2 (27B, under 6GB, WebGPU-runnable) exists to put a capable model inside the app rather than behind an API. For a builder running heterogeneous routing, the practical read is that the Haiku-tier slot in your router is becoming a schema-constrained call you can verify, not a prompt you have to re-parse.
2026-09-05 / reddit-researcherBase-3 packing for ternary GGUFs cuts weight VRAM about 22% with no precision lossA new llama.cpp fork (based on commit 4e97ac86e) adds Q2_B3, a format that packs ternary weights directly in base 3: 128 weights per block become 26 bytes of trits plus one f16 scale, 28 bytes total, or 1.75 bits per weight. On genuinely ternary models such as BitNet-b1.58 or Ternary-Bonsai that takes a 9B from roughly 2.5GB to 2.0GB and a 27B from 7.6GB to 5.9GB, weights only. The stated caveat matters: this is not a general 2-bit quantizer, feeding it an FP16 model destroys quality, and the CUDA path compiles but is unverified since the author only owns a 7900 XTX.

Graph relationships

BENCHMARKED AGAINST
Qwen3.8-27B
Qwen3.8 27B IQ3_XXS produces 27,197 output tokens while Ternary-Bonsai-2-27B-PQ2_0 produces 84,176 on the same tasks.
Source finding
BENCHMARKED AGAINST
Qwen3.8-27B -> Ternary-Bonsai-2-27B
Qwen3.8 27B IQ3_XXS produces 27,197 output tokens while Ternary-Bonsai-2-27B-PQ2_0 produces 84,176 on the same tasks.
Source finding

Source trail

danmoreng.github.io (via r/LocalLLaMA, 77 upvotes)Hugging Face / Prism ML (via r/LocalLLaMA, 1,347 upvotes)r/LocalLLaMA r/LocalLLaMA

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