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Espresso is a Swift library that compiles MIL programs straight for the Neural Engine of Apple Silicon Macs through private APIs, without Core ML. On a 6-layer model on an M3 Max it generates 519 tokens per second against 152 for Core ML; with real models such as Qwen2.5, attention stays on the CPU.
NPUs stopped being an accessory and became the component that defines real performance in laptops, phones, and small servers. A practical look at the hardware that rules 2026, which workloads pay off, and where the traditional GPU still wins.
Neural processing units have stopped being a marketing label on Snapdragon, Apple Silicon and AMD Ryzen AI laptops. Here is what you can actually do from code today, which tools are mature, and when it pays off to target the NPU instead of the CPU or GPU.
Qualcomm, Intel and AMD Copilot+ processors have normalised the presence of an NPU in everyday PCs. A 40 TOPS NPU can run quantised Phi-3 Mini drawing just 5-10 W, versus 40-50 W for a laptop GPU doing the same task. What actually changes for running AI models locally, and when it is worth it.
Qualcomm Snapdragon X Elite is the first ARM chip to match Apple M3 performance in Windows laptops, with 22+ hours of battery life and a 45 TOPS NPU for on-device AI. Most software runs well via the Prism emulator, though it is not yet ready for anti-cheat gaming or specialized x86 workloads.
