Your Edge AI Copilot & Command Center
RunLocal is the first AI-native platform for porting PyTorch models to edge devices like Qualcomm and Nvidia Jetson
Automate The Painful Parts Of Porting
RunLocal accelerates model optimization with a specialized AI agent and integrated environment that handles the complexity
Obscure Debugging
Deciphering cryptic errors across model graph transformations throughout porting and massive profiling logs
Automatic parsing of model graphs, on-device profiling traces, chip vendor SDKs, and more for the agent to fix the root cause
Trial-and-Error Optimization
Endless experimentation to find optimal trade-offs with unclear signal on what's driving results
Lineage system enabling the agent to explore hypotheses, accumulate precise insights, and iterate at scale
Makeshift Pipeline Scripts
Porting and validation scripts that hide dependencies, frequently break and force full reruns
Graph-based orchestration with explicit dependencies that are inspectable and resumable from any node with a webUI
Leaky Experiment Tracking
Manually tracking experiments in local folders and inevitably losing history, lineage and insights
Web dashboards that enable your team to track everything and keep insights, like WandB/MLflow for edge AI porting
Better Than Generic AI Coding Tools
RunLocal extends AI coding agents with an environment tailored to edge AI porting nuances, not generic software development
Generic AI Coding Tool
(Cursor, Claude Code, MS Copilot)
AI Coding Agent
Autonomous LLM-powered agent planning and implementing code changes
Source Code Native
Reads and writes source code directly within your existing repositories
Graph Based Orchestration
Replaces writing scripts with a visual graph system that is better suited for managing the DAG-like validation pipelines inherent to porting
Porting Artifact Native
Intelligently injects signal from model graphs, on-device profiling traces and other artifacts that are fundamental to porting to guide the agent
Experimentation Lineage
Structured schema that intelligently maps changes to porting metrics and insights for the agent, eliminating the noise of generic code change history
Compounding Knowledge
Persistent knowledge base that accumulates empirical insights over time for the agent, exploiting the similarity across use cases in porting
Vendor SDK Knowledge
Pre-codified configuration skeletons of QNN, TensorRT, etc. to constrain the agent and prevent hallucination, rather than naive DIY context injection
Device & Compute Management
Built-in infra and web UI for discovering, pooling and queuing your target devices, plus dispatching porting steps to appropriate compute nodes
Backed By
and more