"Deploy fast, private AI models across iOS, Android, and edge devices — with just a few lines of code."
TL;DR: Run Multi-modal AI fully on-device with one SDK and manage model rollouts + policies from a control plane. They are already live and open source with ~3.9k stars on GitHub.
Edge AI is inevitable — users want instant responses, full privacy (health, finance, personal data), and AI that actually works on planes, subways, or spotty rural connections.
But shipping it today is brutal:
Every device (iPhone 14 vs Android flagship vs low-end) has wildly different memory, thermal limits, and accelerators.
Teams waste quarters rebuilding model download/resume/unzip/versioning, lifecycle (load/unload without crashing), multi-engine wrappers (llama.cpp, ONNX, etc.), and cross-platform bindings
No real observability — you're blind to fallback rates, per-device perf, crashes tied to model version
Result: most teams either give up on local AI or ship a brittle, hacked-together experience.
The Solution: Complete AI Infrastructure
RunAnywhere isn't just a wrapper around a model. It is a full-stack infrastructure layer for on-device intelligence.
1. The "Boring" Stuff is Built-in They provide a unified API that handles model delivery (downloading with resume support), extraction, and storage management. You don't need to build a file server client inside your app.
2. Multi-Engine & Cross-Platform They abstract away the inference backend. Whether it's llama.cpp or ONNX etc, you use one standard SDK.
iOS (Swift)
Android (Kotlin)
React Native
Flutter
3. Hybrid Routing (The Control Plane) They believe the future isn't "Local Only"—it's Hybrid. RunAnywhere allows you to define policies: try to run the request locally for zero latency/privacy; if the device is too hot, too old, or the confidence is low, automatically route the request to the cloud.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.