The AI agent for model deployment, inference optimization and hardware acceleration
(e.g. Pre/Post-Processing)
Autonomous optimization for your target hardware
RunLocal experiments with graph rewrites, quantization, custom kernels, runtime flags and more, benchmarking each on your target hardware. Each run deepens the agent's understanding of how your model behaves on-device — and how to improve performance or fix issues like unsupported operators.
Why teams choose RunLocal
Better Performance
Faster runtime, same accuracyFaster Timelines
Deploy in days, not weeksLess Manual Work
AI agent executes, you overseeAvoid Costly Deployment Bottlenecks
RunLocal diagnoses on-device performance issues and inference bottlenecks (e.g. unsupported layers), and then iteratively experiments with model graph changes, quantization, custom kernels, runtime flags, and more to improve on-device runtime and accuracy
Obscure Debugging
Performance drop-offs quantizing certain layers, pre/post-processing latency bottlenecks, and other silent failures that require manually digging into model graphs, profiling logs, etc.
An AI agent explores dozens of different fixes (model graph surgery, custom kernels, etc.) and finds the best one, while you focus on less frustrating and cumbersome work
Endless Trial-and-Error
Manually experimenting with model graph changes, quantization configs, custom kernels, SDK flags and more to improve on-device performance
An AI agent autonomously experiments with inference optimizations while you sleep, and lets you know the best runtime-accuracy trade-offs
Better Than Generic AI Coding Agents
RunLocal's environment (context management, tools and guardrails) enables coding agents to explore the optimization space better, faster, and cheaper
Artifact Lineage Tracking
Git-like version control over experimentation data. Tracking every change, artifact (model binary, benchmark result, etc.) and how they all connect
Bayesian Causal Graph
Turns experimentation data into an understanding of how specific changes might affect performance, i.e. the agent's “mental model” as it experiments
Knowledge Base
Persistent memory of what works and what doesn't on specific hardware
Chip Vendor SDK Encoding
Curated references and commands so the agent doesn't invent or hallucinate flags
Dockerized Environments
Reproducible environments the agent mounts into, so HW/SW dependencies are set up correctly and so results are comparable across runs
Managed device execution
Handles on-device benchmarking queuing, dispatching, retries, etc., so that hundreds of experiments overnight run reliably
Backed By
and more