This is the systems truth under every on-device model: performance comes from using the right processor for each operation, and which processor that is depends on the operation's size and the hardware present. The cost of an operation on a device is roughly a fixed dispatch overhead plus the work divided by that device's throughput. The CPU has low overhead and modest throughput; the GPU has high overhead (encode a command, submit it, synchronize) and enormous throughput; an NPU sits elsewhere again. So the fastest device is a function of the workload: below a crossover size the low-overhead CPU wins, above it the high-throughput GPU wins, and a smart runtime measures those numbers on the actual box and routes each op accordingly, keeping results deterministic across backends. That is why the tiny models in this course correctly run on the CPU here, routing them to the GPU would be slower, while the very same code sends a large network, a batched training run, or a real vision-language-action model to the GPU automatically. It is also why 'no GPU needed' was the wrong framing: the right framing is 'use the GPU when it is the right tool', decided per workload. Ferric proves this fabric runs one pure-Rust source bit-identically across Metal, Vulkan, and the browser's WebGPU (the on-device, zero-lock-in substrate physical AI needs. In the browser your fabric has two devices) CPU-wasm and WebGPU. On the full native runtime the same law routes across four: the CPU; the GPU through the portable path; the GPU's tensor units, reached through raw Metal 4 because no portable API exposes them, running the same matmuls up to ~14× faster; and the Apple Neural Engine, reached through CoreML, and added to the fabric only when Apple's own compute-plan receipt confirms the Neural Engine actually runs the work. Measured on an M5 Max: the tensor units sustain trillions of operations per second where the portable path manages hundreds of billions, and the ANE lands at ~700 GFLOP/s with the highest dispatch overhead of the four, so on that machine the router never picks it. On a phone, where the NPU is the strong unit, the same measurement flips the answer. That is the entire doctrine in one sentence: the fabric does not assume what your silicon is good at, it measures, and the numbers decide.