At its core, on-device AI means that the AI model (the brain that processes and responds to your request) runs directly on your device rather than on a remote server.

Traditionally, most AI-powered features you’ve used were cloud-based: your request would travel from your device to a server, the AI would process it there, and the result would be sent back. With on-device AI, everything happens locally.

This isn’t just a theoretical shift, it’s happening right now:

• Apple Intelligence is set to become an integral part of iOS and macOS, designed to handle personal, context-aware tasks while protecting user data.
• Google Gemini Nano is Google’s lightweight AI model built specifically for Android devices, enabling features like summarisation and smart replies without a network connection.
• TensorFlow Lite is an older but battle-tested framework that allows developers to deploy machine learning models directly onto devices with constrained hardware.
• XCutorch from Software Mansion is a cross-platform PyTorch-based framework designed to run efficiently on-device across iOS, Android, and beyond.

While these platforms differ in capability and focus, they share the same ambition: to make AI faster, more private, and more reliable for everyday use.

It’s not just about faster processing (although that’s a huge win). The move to on-device AI changes the fundamentals of app reliability and trust.

Think about it: when the exact same AI model is installed and running on every device, users get a consistent experience. Whether they’re on a train with patchy Wi-Fi, working from a rural location with no 4G, or travelling abroad without roaming data, the AI behaves the same way.

For us as app developers in the UK, this matters because it gives us more control over the end-user experience. We’re no longer at the mercy of a bad signal or a busy server, and our users can rely on features working exactly as promised.

It also has serious privacy implications. On-device AI processes data locally, meaning sensitive information doesn’t need to be sent over the internet to a third-party server. For sectors like healthcare, finance, or travel (where privacy isn’t just important but essential), this is a game-changer.

Like any technology shift, on-device AI comes with advantages and trade-offs.

On-device AI offers clear benefits that are hard to ignore:

• Low latency - Responses happen almost instantly because there’s no need to send data back and forth to a server.
• Privacy and security - Data stays on your device, significantly reducing exposure to potential breaches.
• Offline functionality - Features work anywhere, even without a network connection.
• Consistency - Performance is reliable regardless of network conditions.

But it’s not without its limitations:

• Hardware constraints - Mobile devices simply can’t match the raw computing power of cloud infrastructure.
• Battery usage - Running AI models locally can be power-intensive.
• Update challenges - Updating AI models on millions of devices can be slower than pushing an update to a single cloud system.
• Development complexity - Designing and optimising AI to work efficiently on a wide range of devices is no small feat.

For app companies in the UK, the skill is in maximising the strengths of on-device AI while carefully mitigating the weaknesses.

It’s important to note that on-device AI isn’t replacing cloud AI altogether, far from it. The most forward-thinking AI systems take a hybrid approach:

• Use on-device AI for tasks that need speed, privacy, and offline capability - such as voice commands, predictive text, or personalisation features.
• Fall back to cloud AI for resource-intensive tasks - like processing huge datasets, running complex queries, or leveraging cutting-edge models that are too large for a phone to handle.

This combination gives users the best possible experience: fast, private responses for everyday interactions, with the option to tap into the vast power of the cloud when needed.

For app programmers in the UK, this means designing apps with flexibility in mind. The app should feel lightning-fast and reliable most of the time, but seamlessly switch to cloud processing for those “heavier” moments.

From our perspective as application developers, on-device AI doesn’t just tweak our workflows; it fundamentally changes them.

It pushes us to:

1. Design for offline capability - Users shouldn’t be penalised for having a poor signal.
2. Prioritise privacy-first design - Sensitive information never needs to leave the device unless absolutely necessary.
3. Optimise for hardware constraints - We have to make AI features work efficiently on a variety of devices, from flagship models to more budget-friendly hardware.
4. Think in terms of consistency - The AI experience should be the same whether the user is online, offline, in London, or on the other side of the world.

For industries like:

• Travel - AI-powered itinerary updates or translations that work on the go without roaming charges.
• Healthcare - Patient data processed locally to maintain confidentiality.
• Retail - Product recognition tools that work instantly in-store, even without Wi-Fi.
• Field operations - AI-based diagnostics or checklists available in remote locations.

This isn’t a small change; it’s a whole new design philosophy.

On-device AI represents a shift in both technology and mindset. It’s about delivering speed, privacy, and reliability in a way that cloud-only AI can’t match. But it’s also about knowing when to combine it with cloud power to give users the best of both worlds.

At The Distance, we’re already exploring how on-device AI can enhance the apps we build, from AI-powered travel tools that work anywhere to healthcare applications where privacy is paramount.

The truth is, the future of AI won’t just live in the cloud. It will live in your pocket, on your device, ready to help you wherever you are.