RunAnywhere is a toolkit for running AI models entirely on-device — LLMs, vision, speech-to-text, and text-to-speech — across iOS, Android, Flutter, React Native, and Web from a single SDK. Its premise is that a large and growing class of AI features should never leave the user's hardware: they should be private, work offline, and respond with local latency. The project has passed 10,000 GitHub stars and is actively developed, and it distinguishes itself from the many local-inference libraries by treating cross-platform reach and hardware acceleration, especially on mobile NPUs, as the core problem to solve rather than an afterthought. ## What It Is RunAnywhere provides one API surface — a `loadModel` and `generate` pattern — that spans five platforms and routes each call to the best inference engine available on the device. On Apple hardware it uses Core ML, in the browser it uses WebGPU, and it falls back to llama.cpp everywhere else so that no platform is left without a path to run. On top of raw text generation it ships higher-level building blocks, including a full voice-assistant pipeline that chains speech-to-text, an LLM, and text-to-speech. The goal is that a developer writes their AI feature once and it runs locally on whatever device the user happens to have. ## One SDK, Every Platform The unifying idea is that on-device AI is fragmented — every operating system and chip has its own accelerators and formats — and that fragmentation is what stops teams from shipping local features. RunAnywhere absorbs it behind a single interface, publishing SDKs for iOS, Android, Flutter, React Native, and Web. That breadth is the project's central value proposition: instead of maintaining separate Core ML, NNAPI, WebGPU, and CPU code paths, an application targets one API and lets the toolkit pick the right engine per device. Prebuilt, device-matched model bundles are distributed through Hugging Face, and the SDK downloads the variant that fits the target hardware. ## Hexagon NPU Acceleration RunAnywhere's most technically ambitious component is QHexRT, its runtime for the Qualcomm Hexagon NPU. It runs LLM, vision-language, speech-to-text, and text-to-speech models directly on the Snapdragon NPU (Hexagon v79 and v81) and behaves as a built-in accelerator: the same `loadModel` and `generate` calls transparently use the NPU on supported devices. Notably, it runs text-to-speech on the NPU, which many runtimes still push to the CPU, and it supports Mixture-of-Experts and hybrid-attention models such as Phi-tiny-MoE and Qwen3.5. The project publishes measured numbers on a Samsung Galaxy S25 — for example, a 0.23B model decoding at 164 tokens per second with a 32-millisecond time to first token — which is the kind of concrete, device-level reporting that on-device claims usually lack. ## Real Workflows The use cases RunAnywhere targets are practical and consumer-facing: private chat assistants that work on a plane, on-device transcription, local voice assistants, and vision features that never upload an image. Because the SDK spans mobile, desktop-web, and cross-platform frameworks, the same feature can ship in a native iOS app, an Android app, and a React Native or Flutter codebase without re-implementing inference three times. The published App Store and Google Play demo apps show the toolkit running in real, shipped applications rather than only in benchmarks. ## Usability in Practice For mobile and cross-platform developers, RunAnywhere is designed to lower a genuinely hard bar: shipping local models on phones. The single-API abstraction and prebuilt model bundles remove much of the per-platform plumbing, and NPU acceleration is opt-in-by-default on supported hardware rather than something the developer must wire up. The honest caveats are real, however. The most important is licensing: the project is released under a custom "RunAnywhere License," which makes the source available but is not a standard OSI-approved open-source license, so teams must read the terms before commercial use. Beyond that, the strongest hardware acceleration (QHexRT) is specific to Qualcomm Snapdragon devices, on-device inference is inherently bounded by phone memory and thermals, and larger models remain impractical on constrained hardware. ## Pros and Cons The strengths are a single API across iOS, Android, Flutter, React Native, and Web; automatic per-device engine routing with a universal llama.cpp fallback; genuine NPU acceleration for LLM, VLM, speech, and TTS on Snapdragon; support for MoE and hybrid-attention models on the NPU; transparent, device-level benchmarks; and privacy and offline operation by construction. The trade-offs are the non-standard source-available license, the concentration of top performance on Qualcomm hardware, the inherent limits of on-device compute for large models, and the young, fast-moving nature of the codebase. ## Outlook RunAnywhere is riding a real shift toward local and private AI, driven by privacy expectations, offline requirements, and the arrival of capable mobile NPUs. A toolkit that hides platform fragmentation behind one API and squeezes real performance out of on-device silicon is well matched to that moment, and the breadth of SDKs plus published hardware numbers suggest genuine engineering depth. The main thing to watch is the license: broader adoption by commercial teams will depend on how permissive the terms prove in practice. ## Conclusion RunAnywhere is a capable, ambitious toolkit for running AI locally across virtually every client platform, with standout Qualcomm NPU acceleration. It is most compelling for mobile and cross-platform developers who need private, offline, low-latency AI features and want to write them once. Teams should weigh the custom source-available license against their needs, but for on-device inference in 2026 it is one of the more complete options available.