Bonsai 27B (1-bit LLM): The First 27B-Class Model to Run on a Phone
The Bonsai 27B model has been released as the first 27B-class AI model capable of running locally on a smartphone. By utilizing 1-bit and ternary weight quantization, the model achieves high-level reasoning and agentic capabilities within a small memory footprint.
Why it matters
This represents a major breakthrough in edge computing, enabling powerful AI capabilities to function offline on consumer mobile devices without relying on cloud servers.
Today, we're announcing Bonsai 27B, based on Qwen3.6 27B, the new multimodal flagship of the Bonsai family and the first model of its capability class to run on a phone.
Our earlier releases proved that models with 1-bit and ternary weights could produce commercially useful language models. Bonsai 27B extends that frontier to a new capability tier: multi-step reasoning, structured tool calls, vision tasks, and computer-use agentic loops that stay coherent across many steps. Until today, deploying that tier locally has been impractical for a concrete reason: a 27B model occupies roughly 54GB in 16-bit precision, and even a good 4-bit build, at 18GB, is too large for a phone and for most laptops.
Bonsai 27B changes that. It comes in two variants:
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