Developer bypasses Apple's restrictions to unlock the M4's true AI potential

Apple's M4 processors pack a massive amount of AI computing power, but the company has historically kept the hardware tightly locked down. By default, the Neural Engine inside the M4 is restricted entirely to inference. This means developers can only use it to run pre-trained AI models rather than actually training new ones from scratch.

However, a developer has managed to bypass these strict software limitations, fully reverse-engineering the chip to unlock 15.8 TFLOPS of hidden AI crunching power. The breakthrough comes from a researcher known as 0x0SojalSec, who recently shared code on GitHub detailing how they tapped into the true potential of the M4. What makes this achievement particularly impressive is that it was done completely outside of Apple's official development ecosystem.

Because Apple does not grant the necessary permission levels to communicate directly with the Neural Engine for these advanced tasks, the developer had to figure out a way to work without using standard tools like CoreML, Metal, or even relying on the graphics processing unit. To pull this off, they built a custom Model Intermediate Language from the ground up. This custom software successfully bridged the gap, allowing for full backpropagation and transformer training directly on the Apple Neural Engine.

Since the hardware is heavily restricted by design, the developer also had to use some very clever workarounds to keep the system stable. For example, if a process gets stuck during the intensive training phase, the custom language uses a specific execute command to essentially respawn the process. This allows the system to refresh its current state and pick the machine learning right back up without crashing the entire program.

Speed was also a major factor in getting this heavy workload to run effectively. To ensure the training operated as smoothly as possible, the developer configured the process to write everything entirely to the system RAM. By actively avoiding the much slower NAND flash storage, the entire operation remained incredibly fast. For anyone using an M4 equipped Mac or iPad, this fascinating workaround proves that the silicon is more than capable of handling heavy duty AI training workloads, even if Apple officially prefers to keep those specific capabilities locked away.