The AI agent for model deployment, inference optimization and hardware acceleration
(e.g. Pre/Post-Processing)
RunLocal Agentic Environment
Specializing generic coding agents with tools, context management and orchestration for on-device optimization. As the agent experiments with model graph rewrites, quantization and more, RunLocal refines experimentation data into an understanding of what drives performance - enabling better, faster and cheaper optimization.
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 off-the-shelf coding agents - with tools, context management and orchestration specifically for on-device optimization. Connect directly to your repos and target hardware for seamless integration, and deployed in your own infra for maximum security.
Avoid Costly Bottlenecks
RunLocal enables faster time-to-market, more optimized models in production and less engineering cost.
Performance Bugs
Scratching your head with unsupported model layers, performance drop-offs from quantizing certain layers, and other silent 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
RunLocal unlocks robust hardware-in-the-loop experimentation and and continuous learning. As the agent experiments with graph rewrites, quantization, custom kernels and more, RunLocal refines experimentation results 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