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 streamlines model porting 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
A multi-agent AI system that's fed with parsed model graphs, on-device profiling, chip vendor SDKs and more to debug issues
Trial-and-Error Optimization
Endlessly experimenting to find optimal trade-offs without clear signal on what's driving results
A multi-agent AI system that hypothesizes and iterates continuously, with a lineage system to learn from past experimentation
Brittle Pipeline Scripts
Maintaining porting and validation scripts that hide dependencies, frequently break and force full reruns
A graph-based orchestration system and web UI with explicit dependencies that are inspectable and resumable from any node
Leaky Experiment Tracking
Manually logging and 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 goes beyond AI coding agents, with a platform tailored to edge AI porting, 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 DAG-like validation pipelines inherent to porting
Porting Artifact Native
Intelligently injects context for the agent from model graphs, on-device profiling traces and other artifacts that are fundamental to porting
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
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