Most iOS AI features should start on-device: it is faster, cheaper at scale, works offline, and keeps sensitive data local. Use the cloud only when the workload truly needs heavyweight models—and design for the failure modes that happen when networks, models, or power states change.
🧠The Decision Framework
Start with constraints, not model hype. Three pillars decide where intelligence should live: latency, cost, and reliability plus privacy.
- →Latency: Real-time UI (camera, gestures, sliders) cannot tolerate network variability; cloud round-trips kill the experience.
- →Cost: Cloud is usage-based billing, so success scales your bill. On-device shifts compute to the user's silicon.
- →Reliability + Privacy: Local models keep working underground, on planes, and with sensitive data that never leaves the device.
⚙️The Silicon Reality
On-device ML is resource management. You are load balancing between CPU, GPU, and ANE while keeping the UI smooth and the battery cool.
CPU
Great for orchestration, poor for tensor math; heavy models here mean heat and jank.
GPU
Strong at parallel work, especially vision pipelines that pair well with the Vision framework.
Apple Neural Engine
The power-efficient specialist for tensor workloads; ideal for most Core ML inference when model size fits.
Treat compute unit selection like a routing decision: prefer ANE, fall back to GPU, and only use CPU when the model is tiny enough that transfer overhead dominates.
🛠️Model Size, Memory, and Quantization
Jetsam will kill your app under memory pressure. Model size is product-critical, not just a build artifact.
- Quantization trades some precision for lower memory and faster inference (32-bit → 16-bit → lower-bit).
- Smaller models often start faster on CPU because ANE transfer overhead can outweigh gains on tiny networks.
- Bundle preprocessing parameters and label maps with the model so inputs match training expectations.
Think of quantization as shipping a compressed texture: a touch less fidelity, but the framerate—and the user experience—stay high.
🍎Hybrid Is Powerful—and Fragile
Use on-device for instant, private tasks (OCR, tagging, gesture cues). Reach for cloud only when the task truly needs heavyweight models or server-side context.
⚠️ WARNING: The Missing Model Trap
If your offline fallback depends on a model delivered via on-demand resources, a user without connectivity cannot fetch it—and cannot reach your cloud API. The app becomes a brick when it is supposed to shine.
Guardrail: Treat local models like dependencies. Verify presence at launch, prefetch when online, and surface a clear degraded mode when they are unavailable.
A resilient hybrid stack starts local, escalates to cloud when needed, and always communicates state to the user (offline, low power, degraded accuracy).
✨This Week's Action Plan
- ✓Write a failure matrix: Document behavior for no internet, API downtime, missing model, low power mode, and memory pressure.
- ✓Define degraded UX: Show clear states and recovery steps instead of silent errors when AI features pause or reduce quality.
- ✓Audit model loading: Verify local models at app start, prefetch when online, and log fallbacks when compute units change.
- ✓Choose a default architecture: Start on-device; add cloud intentionally for the few tasks that truly need it.
🎯Key Takeaways
- 1.Start local by default—Latency, privacy, and cost usually favor on-device Core ML.
- 2.Respect the silicon—Route work between ANE, GPU, and CPU based on size and overhead, not just defaults.
- 3.Manage memory like a budget—Quantize, right-size models, and include preprocessing contracts to avoid silent failures.
- 4.Design hybrid with guardrails—Prefetch local models, surface degraded modes, and never depend on a download to be "offline ready."
About Sandboxed
Sandboxed is a podcast for iOS developers who want to add AI and machine learning features to their apps—without needing a PhD in ML.
Each episode, we take one practical ML topic—like Vision, Core ML, or Apple Intelligence—and walk through how it actually works on iOS, what you can build with it, and how to ship it this week.
If you want to build smarter iOS apps with on-device AI, subscribe to stay ahead of the curve.
Ready to dive deeper?
Next episode, we explore How Machines "Learn" from Data—bridging training vs. inference and what that means for the models you ship.