Prism ML releases Bonsai 27B, a 3.9 GB 1-bit model that fits an iPhone 17 Pro

Prism ML has released Bonsai 27B, a compressed version of Qwen3.6 27B for Mac, iPhone, iPad and NVIDIA GPUs. The 1-bit variant needs 3.9 GB, which Prism says fits within the memory budget of an iPhone 17 Pro. The company notes that a phone never exposes its full memory to an app and that a 12 GB iPhone offers about 6 GB to a model.

The ternary variant stores each weight as -1, 0 or +1 and takes up 5.9 GB at 1.71 effective bits per weight. The 1-bit variant stores weights as -1 or +1 at 1.125 effective bits. Prism applies the low-bit format to embeddings, attention layers, MLPs and the output head. Both models accept images through a 4-bit vision tower and offer a 262K-token context window.

Across a 15-benchmark suite, Prism reports an overall score of 85.0 for Qwen3.6 27B, 80.5 for the ternary model and 76.1 for the 1-bit model, which it describes as retaining 95% and 90% of the original. The largest gaps show up in agentic tool calling, at 80.0 for the original, 74.0 for ternary and 66.0 for 1-bit, and in vision, at 72.6, 65.2 and 59.6. Prism also publishes an “intelligence density” figure, defined as the negative log of the error rate divided by model size in GB, of 0.530 for the 1-bit model and 0.400 for the ternary one.

On an NVIDIA GeForce RTX 5090, Prism measures 163 tok/s for the 1-bit model and 134 tok/s for the ternary model. On an Apple M5 Max the figures are 87 and 58 tok/s. The models also support speculative decoding, where a small draft model proposes tokens that the main model then verifies.

Bonsai 27B is released under the Apache 2.0 license, with builds for MLX on Apple devices and for CUDA on NVIDIA GPUs. Weights are in the Hugging Face collection, and the Bonsai-demo repository covers running them. Prism also lists the Locally AI iOS app and a free, limited-time developer API on Together.ai as ways to try the model.