# PrismML Squeezes Qwen3.8 27B Into 5.9GB With Ternary Weights, Open Under Apache 2.0

PrismML's Ternary Bonsai 2 27B stores Qwen3.8 27B at 1.76 effective bits per weight: 5.9GB, more than 9x smaller than full precision, at a claimed 98.2% of the full model's aggregate score. The weights are out under Apache 2.0, and every figure is PrismML's own.

By Neomanex. Updated 2026-10-13.

**Our verdict: conditional, so download it and test it.** [Ternary Bonsai 2 27B](/models/ternary-bonsai-2-27b) puts [Qwen3.8-27B](/models/qwen3-8-27b), a 262K-context text-and-image model, into 5.9GB under Apache 2.0. That is small enough for a single consumer GPU, a Mac, or even an iPhone or iPad. The catch: every number below comes from PrismML, and a 98.2% aggregate can hide a bigger drop on your task. If you run local models, this is worth an afternoon on your own workload before it replaces anything.

## What happened

On 17 September 2026, PrismML released Ternary Bonsai 2 27B, weights on Hugging Face and GitHub under Apache 2.0. It stores Qwen3.8 27B as ternary weights (each -1, 0 or +1, with FP16 group-wise scaling), for 1.76 effective bits per weight. The result is 5.9GB, which PrismML says is more than 9x smaller than full precision.

PrismML's figures:

| Measure | Value |
|---|---|
| Aggregate score | 83.9, against 85.4 for full precision (98.2% retained) |
| Size | 5.9GB at 1.76 effective bits per weight |
| Context | 262K tokens |
| Input | Text and image |
| Throughput | Up to 143 tokens/s on RTX 5090, 46.8 tokens/s on M5 Max |
| Energy | 0.714 mWh per token on RTX 4090 |
| License | Apache 2.0 |

It runs on NVIDIA GPUs through CUDA, and on Macs, iPhones and iPads through MLX with PrismML's custom low-bit kernels.

## Why it matters

At 5.9GB, a 27B open model with 262K context and image input fits the memory budget of a single consumer GPU or a Mac, and the Apache 2.0 license allows commercial use. If the 1.5-point aggregate gap holds on real work, local deployment of this model class gets far cheaper.

That "if" is why we hold it at conditional. Every figure is PrismML's own, and we have not tested it. An aggregate score averages many benchmarks, so a small average drop can mask a much larger drop on coding, vision or long-context tasks. The base model's own benchmarks are also vendor-reported, which is why [Qwen3.8-27B](/models/qwen3-8-27b) sits at conditional too. You also need PrismML's kernels: a standard runtime will not do.

## What changes for you

- **If you run Qwen3.8-27B locally:** run Ternary Bonsai 2 27B side by side on your own prompts. Swap only if the quality holds on the tasks you actually ship.
- **If a 27B model was out of reach on your hardware:** this is the one to try first. Pull the weights and run them through CUDA or MLX.
- **If you need verified quality:** wait for independent benchmarks. We have not found any yet.

Canonical: https://neomanex.com/news/prismml-ternary-bonsai-2-27b-qwen3-8-5-9gb