The AI agent for model deployment, inference optimization and hardware acceleration
(e.g. Pre/Post-Processing)
Autonomous Optimization Loop
Our AI agent diagnoses on-device performance issues (e.g. unsupported layers) and inference bottlenecks via TensorRT or QNN, and it iteratively experiments with model graph changes, quantization, custom kernels, runtime flags, and more to improve on-device runtime and accuracy.
Why teams choose RunLocal
Better Performance
via TensorRT, QNN, etc.Faster Timelines
Deploy in days, not weeksLess Manual Work
AI agent executes, you overseeAvoid Costly Deployment Bottlenecks
RunLocal diagnoses on-device performance issues and bottlenecks (e.g. unsupported layers), and 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 agentic environment makes generic AI far more effective at autonomously debugging model performance issues and experimenting with on-device inference optimizations
Artifact Lineage Tracking
Git-like version control for experiment data, not code — every artifact (model, profiling log, etc.) is tracked with full lineage of where it came from
Bayesian Causal Graph
Turns experimentation data into an evolving map of how specific changes affect model performance, i.e. the agent's “mental model”
Knowledge Base
Persistent memory of what works and what doesn't on specific hardware
Chip Vendor SDK Indexing
Codified knowledge of vendor toolchains for better reasoning and less hallucination
Dockerized Environments
Managed, reproducible environments the agent mounts into, so it never has to wrangle HW/SW setup, and results stay comparable across runs
Multi-Agent Orchestration
Opinionated split across different agents and jobs (e.g. analyzing profiling logs vs high-level optimization strategy) rather than a single chat loop
Backed By
and more