PrismML's Bonsai 27B: Full reasoning model compressed to fit iPhone

PrismML's release of Bonsai 27B addresses a real constraint in modern AI applications: running models in the cloud accumulates per-token costs and latency with every call, especially when an agent must make hundreds of sequential calls. By moving inference to the device, the marginal cost of each loop drops to zero, and sensitive intermediate data—screen content, documents, tool calls—never leave the user's device. This is the foundation PrismML sees for always-on assistants and privacy-preserving hybrid systems where simple tasks stay local and only hard problems are sent to frontier cloud models.

The compression technique—reducing weights from 16 bits to 1–2 bits across the entire model—is aggressive, yet PrismML's benchmarks show it preserves 90–95 percent of the original Qwen3.6-27B model's performance. Math and coding tasks are nearly unaffected, though image understanding and instruction following show larger drops under the most extreme compression. Apple's interest in licensing this technology is significant: Apple's own on-device models have trailed competitors in performance, and its current Siri implementation requires at least 12 GB of RAM for the most powerful on-device model, offloading complex queries to cloud GPUs. A licensed compression layer could allow Apple to run more capable models locally, narrowing that performance gap while keeping user data private.