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Cataloged from prism-ml/Ternary-Bonsai-8B-mlx-2bit
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Ternary (1.58-bit) language model for Apple Silicon
7.1x smaller than FP16 | 5.2x faster on M4 Pro | 27 tok/s on iPhone | runs on Mac, iPhone, iPad
Each weight takes a value from {-1, 0, +1}, with one shared FP16 scale per group of 128 weights:
The information-theoretic cost is log2(3) ≈ 1.585 bits per weight, plus FP16 group scales (16 bits per 128 weights), for a theoretical minimum of ~1.71 bits/weight. This release uses the MLX 2-bit format, which stores each ternary value in 2 bits plus group scales, for an effective ~2.125 bits/weight.
The addition of a zero value compared to binary (1-bit) provides more expressive weight representations, allowing better preservation of model quality under extreme compression.
Ternary Bonsai 8B runs natively on iPhone and iPad via MLX Swift at 27 tok/s on iPhone 17 Pro Max. The 2-bit format is supported out of the box.
Evaluated with EvalScope v1.4.2 + vLLM 0.15.1 on NVIDIA H100 under identical infrastructure, generation parameters, and scoring. All models are in the 6B-9B parameter range.
Ternary Bonsai 8B ranks 2nd among all compared models despite being 1/9th the size.
For questions, feedback, or collaboration inquiries: contact@prismml.com
w_i = scale_g * t_i, t_i in {-1, 0, +1}pip install mlx-lmfrom mlx_lm import load, generate
model, tokenizer = load("prism-ml/Ternary-Bonsai-8B-mlx-2bit")
response = generate( model, tokenizer, prompt="Explain quantum computing in simple terms.", max_tokens=256,)print(response)density = -ln(1 - score/100) / size_GB@techreport{ternarybonsai, title = {Ternary Bonsai: 1.58-bit Language Models at 8B, 4B, and 1.7B Scale}, author = {Prism ML}, year = {2026}, month = {April}, url = {https://prismml.com}}