Ship smaller models. Make sharper tradeoffs.
NimbleML searches, optimizes, and evaluates models against real device constraints—so edge AI teams can move from dataset to a deployment-ready candidate with less trial and error.
Built openly with design partners. No credit card required.
Optimization target
Balance accuracy, latency, memory, and energy—not one metric in isolation.
Our operating promises
Trust the process, not a marketing benchmark.
Hardware constraints shape the search from the start
Every candidate is compared on measurable tradeoffs
No invented benchmark claims or opaque magic scores
One continuous workflow
From raw dataset to a defensible deployment decision.
NimbleML is designed around the question edge teams actually face: which model is good enough, fast enough, and small enough for this device?
Define the deployment envelope before training begins.
Capture the target device, task, memory ceiling, latency budget, and quality threshold in one experiment brief. The search stays focused on candidates that can survive production constraints.
Search the right model space
Compare architectures, preprocessing choices, and compression strategies against accuracy, latency, memory, and energy constraints.
Validate before deployment
Inspect tradeoffs and export a deployment candidate only when it satisfies the constraints that matter on your device.
4 axes
Accuracy, latency, memory, and energy
1 trace
A reproducible record for every candidate
Your target
Optimization shaped around your hardware
Early-access pricing
Start free. Scale when the workflow proves itself.
Founding users help shape device support, integrations, and optimization priorities. Paid plans begin after launch.
DESIGN PARTNER
Limited cohortCustom
For teams with an active edge ML program
- Multiple projects and collaborators
- Guided hardware-target setup
- Priority device and runtime input
- Direct sessions with the product team
Questions
Before you bring your model
Build with NimbleML
Bring us the model your device cannot run.
Join the early-access cohort and help shape a more practical AutoML workflow for edge deployment.