A researcher built a 5-layer language model optimized for ARM M4 CPUs using native instructions like XOR, popcount, and SDOT instead of traditional neural network operations. The hybrid model achieves 20,882 tokens/second and 0.8786 bits per byte while fitting in 3.36 MB of L2 cache, demonstrating that CPU instruction sets can be repurposed as trainable neural components.