PrismML
Concentrating Intelligence
PrismML develops compact AI models for environments where large models are impractical, including phones, laptops, robots, IoT devices, and efficient data centers. Its Bonsai family uses 1-bit and ternary quantization to reduce memory, speed, and energy requirements while retaining reasoning, tool use, and multimodal capabilities. The company frames its work around increasing intelligence per bit rather than relying on larger parameter counts, with research drawn from Caltech.
The Bonsai lineup includes Bonsai 2 27B, Bonsai 27B, Bonsai 8B, Bonsai 4B, Bonsai 1.7B, and Bonsai Image, each available in 1-bit and ternary variants. Bonsai 2 27B is built on Qwen3.8 27B and targets agentic coding, multimodal reasoning, and long-horizon tool use on local devices. PrismML publishes benchmark comparisons covering intelligence density, throughput, and energy consumption across hardware platforms, with evaluation details in a whitepaper. Models can be downloaded, and a demo and Bonsai Studio are available for trying the models. The pages do not describe a free plan, trial, or pricing structure.
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