The ongoing improvements in smartphone NPUs aren’t automatically translating into smarter AI. Here’s why.
Edge vs. Cloud: Making AI run on a device is not straightforward. While ads tout faster NPUs, many users still rely on cloud-based AI for meaningful tasks. What a phone’s NPU actually does often isn’t clearly explained, so the promised benefits remain mostly theoretical to everyday users.
What an NPU does and why it matters
New consumer processors combine multiple components—CPU cores, GPUs, and imaging controllers—on a single chip (an SoC). NPUs are a newer piece of this puzzle, designed to handle parallel computations efficiently. Qualcomm’s Hexagon NPUs, for example, are a central talking point at product launches, but that branding nods back to a lineage of digital signal processors (DSPs) that began with audio and modem signal processing.
Over time, DSPs evolved to handle more complex tasks like long short-term memory (LSTM) and the matrix operations at the heart of neural networks. As researchers shifted toward convolutional neural networks for computer vision, DSPs increasingly supported the matrix math that AI relies on. Still, NPUs aren’t simply fancy DSPs. They’re specialized for parallelism and large-scale parameter handling, optimized for the transformer architectures common in modern AI.
But there’s nuance: edge AI (on-device) isn’t mandatory for running AI workloads. CPUs can manage light tasks with low power use, GPUs can process more data but consume more power, and NPUs sit somewhere in between. In some use cases—like running AI features alongside a game—the GPU option may be preferable because it avoids shuttling data between an NPU and a graphics engine.
Edge AI is hard and often underutilized
Despite the push for on-device AI, many NPUs sit idle most of the time. The default trend favors cloud AI, especially for large language models (LLMs), which require enormous compute resources and are trained on powerful servers. Even with tiny, on-device models, the cloud often offers superior capabilities because it can host models with hundreds of billions of parameters, far beyond what a mobile device can accommodate today.
On-device models are trimmed down. For example, a ninth-generation NPU might handle about 3 billion parameters, a fraction of the scale cloud models use. Memory limits on phones also force quantization and precision reductions (e.g., FP16 or FP4), which further constrains model size and performance. The practical upshot: edge AI tends to cover narrow, specific tasks like screenshot analysis or calendar suggestions, rather than broad, open-ended reasoning.
Cloud dominance and the hybrid approach
Industry consensus points to a hybrid model: edge processing for fast, private tasks and cloud processing when more power and broader capabilities are needed. This balance allows devices to deliver quick, local results while still leveraging cloud models for more demanding processing. Still, the cloud introduces dependency risks and privacy concerns, since data processed there may be stored or used to improve services.
Personal data and trust
Privacy is a defining concern. Some engineers argue that personalized inferences are best kept on-device to protect user data. But many on-device features still rely on cloud servers for performance and accuracy. This discrepancy means a phone’s default AI features may not be as private as advertised, especially if they require sending data to servers for processing. The risk isn’t just about privacy; it’s also about who controls access to conversations and how they’re stored or exploited.
Reliability and accessibility
Edge AI has reliability advantages: on-device processing doesn’t depend on an internet connection. Cloud services, however, can deliver higher performance and broader capabilities when a stable network is available. Real-world outages or CDN hiccups can make cloud-based AI less reliable than the always-available local alternative in critical moments.
Examples in current devices
Some flagship phones blend edge and cloud AI, but results vary. New hardware with faster NPUs doesn’t automatically yield better outcomes if cloud processing remains the default path for many features. Even manufacturers tout on-device options, yet many services still rely on servers to analyze data or provide enhanced features. In some cases, a company may offer a toggle to run AI entirely on-device, signaling a move toward user control and privacy, though at the cost of feature depth.
What to expect going forward
There’s significant ongoing work to shrink AI models for phones and laptops, and advances in memory capacity and model compression will help edge AI become more capable. Yet, cloud systems will continue to lead in raw compute power and model scale for the foreseeable future. The best user experience will likely remain a blend: lightweight, privacy-conscious edge tasks complemented by cloud-powered features when appropriate.
For users, the takeaway is nuanced: turn on on-device processing when you want privacy and offline reliability, but don’t expect every AI feature to work entirely locally or equally well. If the goal is the highest accuracy and power, cloud-based models still win out—though at the cost of data traversal and potential privacy trade-offs.
What’s your take?
Do you prefer on-device AI for privacy and speed, or do you favor cloud-based AI for power and breadth of capability? Are there specific features you’d like to see redesigned to prioritize privacy without sacrificing usefulness? Share your thoughts and experiences in the comments.
