RunAnywhere
ActiveThe default way of running on-device AI at Scale
About
Edge AI is inevitable, but shipping it is painful: every device class behaves differently, runtimes vary, models are huge, and performance collapses under memory/power constraints. RunAnywhere turns that into an enterprise-ready workflow: one SDK to run models on-device, plus a control plane to manage models, enforce policies, and measure outcomes across thousands of devices.
Founders · 2
Former Intuit engineer building RunAnywhere, the infrastructure layer for deploying fast, private, multimodal AI on-device at scale. Deep background in mobile SDKs, platform tooling, and developer products, including systems used by 50M+ active users. Previously founded products across consumer discovery, context management, agentic documentation, and mobile testing, and now focused on making on-device AI production-ready across mobile, edge, and embedded devices.
AmazonCo-founder & CTO of RunAnywhere (W26). Built MetalRT: the first complete multi-modal inference engine for Apple Silicon. Custom Metal GPU kernels that pushed on-device voice AI from 900ms to ~110ms. Ex-Amazon EC2 Spot ($100M+ ARR), Ex-Microsoft Azure. Peer-reviewed researcher.
Launch
One SDK + Control Plane to Deploy, Route, Update, and Observe On-Device AI – Offline-First, Hybrid-Smart, Fleet-Managed
RunAnywhere provides a full-stack on-device AI infrastructure with a single SDK and a control plane to manage multi-engine models, delivery, and policies. It enables offline-first, hybrid routing for on-device AI across iOS, Android, React Native, and Flutter, targeting developers needing reliable local inference with observed performance and OTA updates.
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