The AI agent for inference optimization that actually understands your target hardware
Minimize onboard latency of your perception/autonomy stack on embedded compute like NVIDIA Jetson.
Stop leaving performance on the table and ship faster — without hiring more inference specialists.
Built for ML teams in
The RunLocal Engine
Purpose-built systems for on-device benchmarking, modeling what influences performance, and managing experimentation context — enabling generic coding agents to squeeze out model performance on your target compute, and hit your performance targets faster.
Secret Sauce In The RunLocal Engine
Unglamorous infra nobody wants to build: precise experiment tracking, trustworthy on-device benchmarks, and performance data that's refined into actionable insights for coding agents
On-Device Benchmark Orchestration
Enables parallel agents to safely share and benchmark on your target device, with numbers you can actually trust — something a naive SSH script can't do. Jobs live in a durable queue; retries never run anything twice; devices are fenced mid-measurement; crashes are detected and retried automatically.
Compute Performance Modeling
Turns experimentation and on-device benchmarking history into a causal and predictive understanding of how model changes affect on-device performance - clarifying both your model's bottlenecks and performance ceiling for your target compute.
Experiment Ledger
Every measurement is bound to what produced it (code change, software versions, input data, etc.) in an append-only record, published by the system not the agent. So you can audit any claim, reproduce any result, and every experiment compounds into knowledge the agent can learn and iterate on.
How You Use It
Install and run our CLI to launch your coding agent in the RunLocal environment, connect it to your real models and target hardware, and prompt it like you normally would — our infrastructure does its magic under the hood.
Integrate
Connect RunLocal to your model repos (code, weights and validation data) and your real target hardware.
Self-hosting our software in your infra is possible to eliminate IP/security concerns.
Optimize
Run our CLI to launch your coding agent in the RunLocal environment, then prompt it exactly like you normally would.
Bring your own AI vendor and API keys (e.g. Codex or Claude).
Audit
Use our Web UI to track the agent's experimentation and verify its output, in real time as it works.
Value To Your Business
More optimized models, shipped sooner, on cheaper compute onboard, with a leaner team.
More Optimized Models
Lower latency and memory (same accuracy), or better models with less compute.
Ship Faster
Hit performance targets in days instead of weeks, or hours instead of days.
Less Headcount & Compute
Avoid hiring rare optimization experts. Downgrade your onboard compute.
Coding Agents Alone Aren't Enough
These failure modes don't go away as coding agents get more capable at generic coding — closing them takes specialized infrastructure built for on-device optimization.
Unreliable On-Device Benchmarking
Benchmarking jobs collide, jobs silently crash, and corrupted runs quietly hinder experimentation.
What it takes
Trustworthy on-device benchmarking, which seamlessly deals with many parallel agents, requires a sophisticated benchmarking system.
Shallow Optimization Hypotheses
Basic optimization hypotheses because they don't have a deep understanding of your target hardware.
What it takes
Cause-and-effect must be derived from benchmarking on real hardware with a specific causal analysis system; better generic reasoning isn't a substitute.
Cheating & Non-Trivial Verification
They find ways to hit performance targets by breaking real constraints, and it's non-trivial to verify results.
What it takes
Changes/results must be precisely recorded/validated independently, and a purpose-built UI is needed for inspecting results and artifacts.
Reduce Costly Bottlenecks
Even with today's coding agents, model inference optimization still drags you into the same grind. With RunLocal, the agent actually handles them for you.
Performance Bugs
Poorly supported layers, unexpected issues after quantizing, and other silent-but-deadly surprises you still end up chasing down alongside your agent.
Endless Trial-and-Error
Babysitting the agent through attempt after attempt, re-explaining context, and hand-holding it toward something that actually hits your numbers.
Missed Performance Gains
Not knowing whether you're near the hardware's limit or leaving speed on the table — and no way to tell if another round of optimization is worth it.
A Continuous Optimization Loop
Your models and validation data go in. The agent hypothesizes, transforms, compiles and benchmarks on real hardware — learning each round until it hits your on-device performance targets.
(e.g. Pre/Post-Processing)
Backed By
and more