The AI Agent for Edge ML
RunLocal is an agentic IDE that automates model inference optimization
and deployment for edge hardware (Nvidia Orin/Thor, Qualcomm, Ambarella, etc.)
Deploy models better, faster and cheaper
Point the agent to your model repo, relevant datasets and target hardware.
It optimizes, validates on-device and iterates until your performance requirements are met
Automate The Painful Parts Of Optimization
RunLocal streamlines model optimization with a specialized AI agent and integrated environment that handles the complexity
Obscure Debugging
Deciphering cryptic errors across model graph transformations throughout optimization 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
Endless Trial-and-Error
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 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 optimization
Better Than Generic AI Coding Tools
RunLocal goes beyond AI coding agents, with a platform tailored to edge AI optimization, 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 edge AI optimization
Optimization Artifact Native
Intelligently injects context for the agent from model graphs, on-device profiling traces and other artifacts that are fundamental to edge AI optimization
Experimentation Lineage
Structured schema that intelligently maps changes to optimization 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 edge AI optimization
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 optimization steps to appropriate compute nodes
Backed By
and more