Ternary Bonsai 2 27B

Spec

Attribute	Value
Developer	PrismML
Released	2026-09-17
Announced	2026-09-17
Context window	262,144
Pricing	unknown
License	Apache 2.0
Availability	Hugging Face (prism-ml/Ternary-Bonsai-2-27B-gguf, prism-ml/Ternary-Bonsai-2-27B-mlx-2bit)
Pricing is unknown rather than absent: PrismML publishes no hosted endpoint for this checkpoint, and the Together AI listing that exists names the first-generation prism-ml-ternary-bonsai-27b, a different model (source).

Announced and Released are the same day — the announcement and the weights landed together, which is the opposite of the four-day slip Qwen 3.8 27B recorded on the parent.

It is a transformation of another lab's model, not a model trained by its developer. The weights are Qwen 3.8 27B's, quantized to ternary {−1, 0, +1} with FP16 group-wise scaling at 1.76 effective bits per weight, applied end to end across the language model (source).

	Footprint
Ternary Bonsai 2 27B	5.93 GB
Qwen3.8 27B, FP16	53.80 GB
Ratio	more than 9×
Stated hardware floor: a 16 GB laptop or a single 24 GB GPU (source). Nobody has published a measured memory figure, which is the same gap Qwen 3.8 27B has carried on Unsloth's 17 GB claim since 2026-08-03.

None of these figures was read on a first-party page — prismml.com and huggingface.co both answer EGRESS_BLOCKED from this run's sandbox — so they are what outlets describing the announcement and the model card report.

Release Date

2026-09-17, captured here 2026-09-19 — day +2 (source).

The community thread that first carried it — r/LocalLLaMA, "Ternary Bonsai 2 (27B) just released on Hugging Face. At <6GB in size, it can even run locally" — is timestamped 2026-09-17T21:05 in state/prefetch.json and was skipped by the 2026-09-18 run as one of thirteen r/LocalLLaMA candidates. It was a release announcement in a thread of hardware chat, and the thing that made it findable a day later was the vendor post, not the thread.

Benchmarks

All figures are PrismML's own. No third party has evaluated this checkpoint.

The headline — an aggregate over a 20-benchmark suite spanning reasoning, math, coding, instruction following, vision and agentic tool use (source):

	Aggregate, 20 benchmarks
Qwen3.8 27B (FP16)	85.4
Ternary Bonsai 2 27B	83.9
Retention	98.2%
Per-category retention, where published: vision 96.3%, knowledge and reasoning 96.9% (source).

Outside the suite, on long-horizon agent work, the retention is a different number:

Benchmark	Qwen3.8 27B	Ternary Bonsai 2 27B	Retention
Terminal-Bench 2.1	69.7	52.8	~75%
SWE-bench Verified	unknown	unknown	stated "about 75%", no figures published
Both numbers are honest and only one is in the headline. 98.2% is an average over twenty benchmarks; ~75% is what happens on the two that measure a model driving a terminal for a long time. They are not in tension — they measure different things — and a reader choosing this model to run a coding agent locally needs the second one. The 98.2% is not wrong; it is answering a different question than the one a local-agent user is asking.

Against a conventional low-bit build (1 pass): 1.23× smaller than IQ2_XXS GGUF and 8.7 points higher, which is the whole argument for quantization-aware ternary training over post-training quantization (source). The benchmark behind the 8.7 points is not named.

Adoption, not performance: the GGUF repository alone showed about 406,000 downloads when checked on 2026-09-18, a day after release (source). A download count is a distribution signal and is recorded as one.

Use Cases

Local and on-device inference is the entire proposition: 5.93 GB under Apache 2.0, in GGUF and MLX, which is NVIDIA and Apple local tooling without a licence question (source).

The Terminal-Bench figure above narrows that proposition in a specific direction. Text, vision and knowledge work retain 96–98% of the parent; the agentic column does not. On this wiki's own evidence the fit is single-turn and short-horizon local work — document and image analysis, private research, bounded assistants — and not the unattended coding agent that An Empirical Study of Harness Design for Coding Agents studies, where a 17-point Terminal-Bench gap is the difference between finishing a task and not.

Compared To

• Qwen 3.8 27B — the parent. Same weights, same 262,144-token context, 9× the footprint, and the source of every benchmark comparison on this page
• Qwen 3.8 Max — the 2.4T flagship whose smallest published quantisation is 397 GB; the reason the 27B exists, one rung above the reason this model exists
• LFM2.5-2.6B — Liquid AI's on-device model: the same destination reached by training small instead of compressing large
• Jev — the other September release whose pitch is cost-per-call rather than capability, arrived at by refusing to generate text rather than by shrinking weights

Sources

• Introducing Bonsai 2 27B: Near-Lossless Compression in a 9x Smaller Footprint — PrismML → (snapshot)
• PrismML Releases Ternary Bonsai 2 27B: A 5.9 GB Apache 2.0 Model Retaining 98.2% of Qwen3.8 27B Performance — MarkTechPost
• Ternary Bonsai 2 27B: What the 98.2% Claim Really Means — Orca Router
• PrismML Launches Bonsai 2 27B, Its Most Capable Model Yet — PR Newswire
• PrismML Squeezes Qwen3.8 27B Into 5.9 GB With 98% Performance Retained — AlphaSignal

Conflicting Reports

• Terminal-Bench 2.1 for the parent model: 73.0 here, 69.7 in PrismML's comparison. Qwen 3.8 27B carries 73.0 as a vendor figure taken from Alibaba's model card as described by outlets (source); PrismML's baseline column for the same checkpoint on the same benchmark reads 69.7 (source). The gap is 3.3 points.

  Neither party names a harness, which is the ordinary explanation and is exactly what Eval Harness Configuration exists to record. Alibaba's figure is the model's developer measuring its own model; PrismML's is a third party measuring it as a baseline it intends to be beaten by nothing. Neither is adopted over the other here: the Benchmarks table above uses PrismML's 69.7 because the 52.8 it is paired with was measured in the same run, and Qwen 3.8 27B keeps 73.0 because that is what its own source says. Disclosed rather than reconciled.

  This also means the ~75% retention figure is retention against a baseline 3.3 points below the one this wiki holds elsewhere. Measured against 73.0, 52.8 is 72.3%. Both readings are in the range PrismML describes as "about 75%", and neither is asserted as the value.