Bonsai 27B: A 27B-Class Model That Runs on a Phone
AI in your pocket: 27B-parameter model comes to mobile devices.
1A Revolutionary Model
Bonsai 27B, based on Qwen3.6 27B, is introduced as the first 27B-class multimodal model capable of running on phones. Previous versions proved that commercially useful models could be created with 1-bit and ternary weights.
2New Capability Frontiers
This model offers new capability tiers including multi-step reasoning, structured tool calls, vision tasks, and computer-use agentic loops that remain coherent across many steps. Previously, running such a model locally was impractical.
3Two Distinct Variants
Bonsai 27B comes in two variants: Ternary Bonsai 27B is the quality-oriented version at 5.9 GB, running on everyday laptops. 1-bit Bonsai 27B is the footprint-oriented version at 3.9 GB, fitting within an iPhone 17 Pro's memory.
4Multimodal Support
Both variants support multimodal inputs. The vision tower ships in compact 4-bit form, enabling on-device processing of screenshots, documents, and camera input. The model also carries a full 262K-token context.
5Performance Comparison
Across 15 benchmarks, Ternary Bonsai 27B retains 95% of full-precision performance, while 1-bit Bonsai 27B retains 90%. Critical areas like math and coding show nearly untouched performance.
6Intelligence Density Breakthrough
1-bit Bonsai 27B delivers 0.53 intelligence per GB - 10x the full-precision baseline and 2.7x better than the best low-bit alternative. This packs 27B-class capability into less space than a full-precision 2B model.
7The Importance of Local Execution
On-device operation keeps user data local and enables zero marginal cost for hundred-step loops. This unlocks persistent on-device agents, offline assistants, and systems that reason over private local data by design.
Original article: Hacker News — Bonsai 27B: Telefonda Çalışan 27B Sınıfı Model