Bonsai 27B: First 27B-Class Model on a Phone

PrismML has released Bonsai 27B, the first 27-billion-parameter class model that fits and runs on a phone. Based on Qwen 3.6 27B, it uses extreme low-bit quantization — ternary or binary weights — to shrink the model to 3.9 GB (1-bit variant) or 5.9 GB (ternary variant), down from 54 GB in 16-bit precision.

Two Variants: Ternary vs 1-bit

Ternary Bonsai 27B: weights in {−1, 0, +1} with FP16 group-wise scaling, 1.71 effective bits per weight. Size: 5.9 GB. Targets laptops with full reasoning, tool calling, and agentic capability.
1-bit Bonsai 27B: binary weights {−1, +1}, 1.125 effective bits per weight. Size: 3.9 GB. Fits in iPhone 17 Pro memory budget.

Both variants run the entire network (embeddings, attention, MLPs, LM head) in low-bit — no higher-precision escape hatches. They support 262K-token context, multimodal vision (4-bit vision tower), and speculative decoding.

Benchmark Scores (thinking mode)

Across a 15-benchmark suite:

Math (GSM8K, MATH-500, AIME25, AIME26): Qwen 3.6 27B 95.3, Ternary 93.4, 1-bit 91.7
Coding (HumanEval+, MBPP+, LiveCodeBench): 88.7 → 86.0 → 81.9
Agentic/Tool-calling (BFCL v3, TauBench): 80.0 → 74.0 → 66.0
Instruction following (IFEval, IFBench): 78.4 → 71.8 → 65.8
Knowledge/STEM (MMLU-Redux, MuSR): 83.1 → 77.0 → 73.4
Vision (MMMU Pro, OCRBench): 72.6 → 65.2 → 59.6
Overall: 85.0 → 80.5 (95% retention) → 76.1 (90% retention)

Math and coding are hit the least — key for agentic workloads. The 1-bit variant at 3.9 GB is smaller than a full-precision 2B model, yet delivers 27B-class intelligence.

Why Local Execution Matters for Agents

Agentic workloads require many sequential model calls — each carrying context and producing structured output. Cloud-only execution means per-step latency, accumulating token costs, and sending private data (screenshots, files) over the network. Running locally eliminates those constraints. Bonsai 27B enables on-device multi-step reasoning, tool calls, and computer-use agentic loops.