The AI agent for inference optimization that actually understands your target hardware
Squeeze more model performance out of embedded compute (like NVIDIA Jetson) than you realize is possible.
Upgrade your onboard perception/autonomy, without hiring more optimization specialists.
Built for ML teams in
The RunLocal Engine
Proprietary performance modeling on your target hardware and on-device orchestration infra (queuing, environment config, etc.), which enable your coding agent to truly understand what's possible with your AI stack on your target hardware, and unlock performance that you wouldn't get otherwise.
Secret Sauce In The RunLocal Engine
Statistical performance modeling grounded in benchmark data we've already collected across real devices, on top of execution infra nobody wants to build. Generic coding agents will never have this, and the value grows as they become generally smarter.
Hardware Performance Modeling
Custom analysis of your model, based on real benchmarks for your target hardware, which feeds into a causal understanding of how model changes might affect on-device performance - clarifying your model's bottlenecks, optimization opportunities and performance ceiling.
On-Device Orchestration
Infrastructure that ensures an agent can safely organize, execute and observe work asynchronously on your target hardware (queuing, HW/SW environment config, etc.)
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