PrismML argues that modern AI apps increasingly need powerful models to run locally. An agent may make hundreds of model calls in sequence, each carrying context, producing structured output, and feeding into the next step. In the cloud, per-token costs pile up, every call adds network latency, and intermediate results, tool calls, and private data such as screen content or documents all leave the device.
But running the model on-device cuts the marginal cost of those loops to zero and keeps user data local. PrismML sees this as the basis for always-on agents, offline assistants, and hybrid systems. Simple and privacy-sensitive tasks stay on-device, while only the hardest steps are sent to frontier models in the cloud.
According to a CNBC report, PrismML is already in talks with Apple about the compression technology behind Bonsai. PrismML CEO Babak Hassibi confirmed that Apple and other companies are testing the models for speed, power draw, and performance. The talks are "very early," but "things are progressing nicely."
Two versions bring the model to laptops and smartphones
A model this size typically takes up about 54 GB of storage. Even with standard compression, it still needs around 18 GB. PrismML offers two much smaller versions: The quality-focused variant takes up about 5.9 GB and is meant for laptops, though the packages currently shipping may be larger depending on the runtime. The white paper lists about 7.2 GB for the llama.cpp version and 8.49 GB for the MLX version.
