Randall added, “as AI-native apps mature and the demand for more performance increases (like how Photoshop increasingly offloads AI to the NPU to free up GPUs and extend battery life), then those benchmarks will become increasingly relevant, especially if on-device private AI (for example, small language models) become commonplace in new device releases.”
As for how fast it actually needs to be, he said, “NPU’s speed matters when it leans into heavier AI workloads. As an example, while little power is needed to blur a background in a video call, image generation pushes the limits of the current NPU’s capabilities. Standardized performance doesn’t matter for most users, especially when increased speed potential would remain underutilized; however, it will matter for developers if they want to scale models with low latency and low power draw.”
According to Randall, because they are purpose-built for AI tasks, NPUs “work great for daily tasks such as speech recognition, background blur during video calls, photo modifications, and even smart capabilities, like Copilot-style assistants. You’ll see NPUs in Apple’s (Neural Engine), Intel’s (AI Boost), and Qualcomm’s (Hexagon).”
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