Concentrating intelligence

Large models can't fit on smartphones. Datacenters can't sustain them. PrismML is building ultra dense intelligence to solve both.
About Bonsai 27B
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14× less memory
8× faster
5× less energy

Bonsai 27B

Available in 1-bit and Ternary variants, Bonsai 27B is the first 27B-class model capable of running on a phone. It brings multi-step reasoning, tool calling, agentic workflows, and multimodal understanding to local devices. Ternary Bonsai 27B is 5.9GB and optimized for laptops, while 1-bit Bonsai 27B is just 3.9GB - small enough to run on an iPhone.

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Supported by:
Benchmark palette

Intelligence density

Negative log of the model's error rate divided by the model size

For detailed evaluation approach, please refer to our whitepaper.

Model benchmark comparison

Average score across all benchmarks

For detailed evaluation approach, please refer to our whitepaper.

Throughput

Tokens per second across hardware platforms (higher is better)

For detailed evaluation approach, please refer to our whitepaper.

Energy consumption

Milliwatt-hours per token across hardware (lower is better)

For detailed evaluation approach, please refer to our whitepaper.
Explore other Bonsai models:

Bonsai Image

1-bit
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Ternary
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Bonsai 8B

1-bit
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Ternary
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Bonsai 4B

1-bit
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Ternary
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Bonsai 1.7B

1-bit
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Ternary
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16.0 GB 16-bit (standard)

1-bit Bonsai 8B

Centering AI research on efficiency

Successful artificial intelligence isn’t just about making models larger, but also smarter. Utilizing breakthrough research at Caltech, PrismML is pushing the frontier of intelligence density by reshaping how models are designed, prioritizing intelligence per bit over sheer parameter count.
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