AI Startup PrismML Releases Bonsai 2: Compresses 27B Model to 5.9GB with 98% Self-Tested Performance Retention

PrismML, an AI startup founded by researchers from Caltech, released Bonsai 2 27B on October 20, 2026. Based on Alibaba's open-source Qwen3.8 27B model, this second-generation compressed model utilizes a unique 'ternary' weight compression technique to reduce the total capacity to 5.9GB, enabling direct execution on personal computers or high-end smartphones.

According to Babak Hassibi, founder and CEO of PrismML, the company's approach aims to preserve performance almost entirely. Internal benchmark results published by the company indicate that while memory usage was reduced by 9 to 10 times compared to the original version, Bonsai 2 27B achieved 98% of the composite benchmark score of the original Qwen3.8 27B, an improvement over the first-generation Bonsai model's 95% score reported six months prior.

Technically, the team simplified each 'weight'—which typically requires 16 bits—to one of three values: +1, -1, or 0, significantly reducing storage requirements. Hassibi acknowledged that compression inevitably impacts performance to some degree, and whether 100% parity can be achieved remains to be seen. He also noted that benchmark scores do not fully reflect real-world task performance, as final accuracy is influenced by software environments and frameworks.

PrismML plans to apply this compression technology to models with hundreds of billions of parameters in the coming months. For consumers and small-to-medium enterprises, high-performance small-capacity models offer free, zero-overhead access to AI capabilities anywhere, anytime, while enhancing privacy by eliminating the need to transmit data to the cloud.