Make your coding agent better at inference optimization and hardware acceleration
Deploy models to edge devices like Nvidia Orin/Thor and Qualcomm — with lower latency inference, less engineering time and without hiring more specialists.
RunLocal Environment
RunLocal supplements off-the-shelf coding agents with purpose-built tools, context management, benchmarking infra and hardware understanding. It makes them smarter and faster at optimizing model inference for edge devices like Nvidia Orin/Thor and Qualcomm.
How You Use It
Run our CLI to launch your preferred coding agent in our environment, connect it to your real models and target hardware, and prompt it like you would normally — our environment does its magic under the hood.
Integrate
Connect RunLocal to your model repos (code, weights and validation data) and your real target device.
Self-hosting our software in your infra is possible to eliminate IP/security concerns.
Optimize
Run our CLI to launch a coding agent in the RunLocal environment, then prompt it exactly like a normal coding agent.
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.
Secret Sauce Under The Hood
An independent layer of tools, context management, benchmarking infra and hardware understanding that supplements coding agents with domain understanding — valuable even as they get better at generic coding.
Experimentation System
The agent leverages our CLI and backend, which manages and records experimentation (optimization hypotheses, implemented changes, on-device results), helping it iterate toward performance targets more effectively.
Bayesian Modeling
Turns experimentation data into a precise, causal understanding of how model changes affect on-device performance, clarifying the performance ceiling and sharpening the agent's optimization hypotheses.
On-Device Infra
Benchmark scheduling across multiple agents, handling of performance profiling, reproducible containerized environments, and so on.
Value To Your Business
More optimized models, shipped sooner, with a leaner team.
More Optimized Models
>30% lower latency with same accuracy, or better models on cheaper chips.
Ship Faster
Hit on-device targets in days instead of weeks, or hours instead of days.
Hire Fewer Specialists
Skip the rare, expensive optimization experts — and save the headcount.
Problems With Generic Agents
These failure modes don't go away as coding agents get more capable at generic coding — closing them takes a purpose-built, on-device optimization layer.
Lazy Hypotheses
Generic agents confuse correlation with causation, so they optimize the wrong levers.
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.
Going In Circles
They forget what they've already tried and keep repeating the same dead ends.
What it takes
Context and insights across experiments, involving complex artifacts (models, logs, etc) not just code, must be managed by a separate, domain-specific system.
Cheating
They hit performance targets however they can, even if that breaks your real constraints (e.g. removing I/O to cut latency).
What it takes
Changes must be validated by an independent system, and you need a purpose-built UI to easily double-check.
Benchmark Chaos
Several agents competing for the same hardware block and corrupt each other's runs and results.
What it takes
Reliable measurement takes a sophisticated device orchestration system underneath.
Reduce Costly Bottlenecks
Even with today's coding agents, model inference optimization still drags you into the same grind. With RunLocal, your agent will actually handle them for you.
Performance Bugs
Poorly supported layers, accuracy drop-offs after quantizing, and other silent-but-deadly issues 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. Your 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