Recent technical articles reveal that AI frameworks like ONNX Runtime and CoreML may automatically convert models to FP16 half-precision format during deployment without clearly informing users. This conversion aims to improve inference speed but can lead to reduced model accuracy, particularly in complex tasks like autonomous driving or medical AI, affecting prediction reliability. The article emphasizes that developers need to be vigilant about this behavior, checking model outputs to ensure performance meets expectations and avoiding production issues caused by silent conversion. This discovery serves as an important warning for AI optimization and deployment practices, reminding us of the critical need to balance precision and speed when pursuing efficiency.
