Run 35B and 80B Qwen models on ordinary Apple devices, including iPhones. Swiftlet is a Swift + Metal runtime for the Qwen3-Next and Qwen3.5/3.6 MoE hybrid model family. It keeps only the small dense core of a model resident in memory and streams the routed Mixture-of-Experts weights from storage on demand. The result: Model Disk Peak RAM Decode speed (M5 Mac) Qwen3.6-35B-A3B, 4-bit 18 GB 2.6 GB 7 to 11 tok/s Qwen3-Next-80B-A3B, 4-bit 42 GB 4.3 GB 4.5 to 5 tok/s The 35B also runs on an iPhone 17 in about 2.5 GB of RAM, at about 1 tok/s today. As far as we know, that is the first time a model of this class has run natively on a phone. Status: working end to end. Both models generate correct, validated output. The current focus is kernel speed (the decode loop is dispatch bound, not IO bound, so there is clear headroom). One expectation to set honestly: only about 3B parameters are active per token, so these models chat and write like large models but recall facts like small ones.…