The AI agent for model deployment, inference optimization and hardware acceleration
(e.g. Pre/Post-Processing)
The RunLocal Agentic Environment
RunLocal specializes generic coding agents for on-device optimization with purpose-built tools, context management and orchestration. It enables robust on-device testing, tracks experimentation, and continously learns what actually drives performance - feeding insights to the agent so that it can optimize better, faster and cheaper.
Why teams choose RunLocal
Better Performance
Faster runtime, same accuracyFaster Timelines
Deploy in days, not weeksLess Manual Work
AI agent executes, you overseeSupercharging Generic Coding Agents
Our environment specializes agents for on-device model optimization. Connect to your repos and target hardware for seamless integration, and deploy in your own infra for data security.
Avoid Costly Bottlenecks
RunLocal enables faster time-to-market, more optimized models in production and less engineering cost.
Performance Bugs
Investigating and fixing poorly supported layers, performance drop-offs after quantizing certain layers, and other silent but deadly issues.
Manual Trial-And-Error
Manually experimenting with model optimizations, getting lost in all the experimentation data, and going around in circles.
Missed Performance Gains
Not knowing if you're near the limit or leaving gains on the table. Optimizing without knowing if further investment is worth it.
Inside The RunLocal Environment
Robust hardware-in-the-loop experimentation and continuous learning. RunLocal refines experimentation into an understanding of what drives performance - enabling better, faster and cheaper optimization.
Experiment Tracking
Git-like version control over experimentation data. Tracking the agent's hypotheses, changes, artifacts, results and learnings.
Bayesian Causal Modelling
Turns experimentation data into an understanding of how specific changes affect performance, i.e. the agent's “predictive model”
Persistent Knowledge
Long-term memory of what works on specific hardware, transferrable across your various optimization projects
Chip Vendor SDK Encoding
Curated references and commands so the agent doesn't invent or hallucinate flags
Managed device execution
Handles on-device benchmarking queuing, dispatching, retries, and more, so that continuous experimentation and testing runs reliably
Dockerized Environments
Reproducible environments the agent mounts into, so HW/SW dependencies are set up correctly and results are comparable across runs
Backed By
and more