@populartourist: I can run a 27B instead of a 9B on my budget laptop. That's mind blowing.
Summary
PrismML announces Bonsai 27B, a multimodal model based on Qwen3.6 27B that can run on a phone, enabling local multi-step reasoning, tool use, and long-context workflows.
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Cached at: 07/15/26, 03:43 AM
I can run a 27B instead of a 9B on my budget laptop. That’s mind blowing.
PrismML (@PrismML): Today, we’re announcing Bonsai 27B: the first 27B-class model to run on a phone.
Bonsai 27B is the new multimodal flagship of the Bonsai family. Based on Qwen3.6 27B, it brings a new capability tier to local AI: multi-step reasoning, structured tool use, long-context workflows,
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@PrismML: Today, we’re announcing Bonsai 27B: the first 27B-class model to run on a phone. Bonsai 27B is the new multimodal flags…
PrismML announces Bonsai 27B, the first 27-billion parameter model able to run on a phone, with ternary and 1-bit variants (5.9 GB and 3.9 GB respectively) that enable multi-step reasoning and agentic workflows on local devices, all open-sourced under Apache 2.0.
Bonsai 27B (1-bit LLM): The First 27B-Class Model to Run on a Phone
PrismML announces Bonsai 27B, a 1-bit and ternary quantized version of Qwen3.6 27B that runs on phones and laptops, retaining 90-95% of baseline performance with a 3.9GB footprint, enabling agentic and multimodal on-device AI.
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Prism ML released Ternary-Bonsai-27B, a ternary-quantized version of Qwen3.6-27B that retains 95% of FP16 intelligence at a ~7.2 GB footprint, enabling full 27B-class reasoning on laptops and single GPUs with speeds up to 26 tok/s on Apple M5 Pro.
@sudoingX: this lab took qwen 3.6 27b, the model i've been calling king of the 24gb tier all month, and crushed it down to 3.9gb. …
PrismML announces Bonsai 27B, a binary-quantized version of Qwen3.6 27B that runs on a phone using only 1.125 bits per weight, claiming 89.5% intelligence retention. The model is being independently tested by @sudoingX to verify performance.
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Bonsai-27B is a 1-bit binary transformer model that achieves full 27B-class reasoning on a phone (iPhone 17 Pro Max) with ~3.9 GB footprint and ~11 tok/s, retaining ~90% of FP16 intelligence.