Stop Paying the Cloud Tax. Move Inference On-Device.
OnDevice determines how much of your cloud-based LLM traffic can move on-device while preserving application quality, latency, reliability, and power requirements.
Your application runs inference entirely in the cloud, drawing on open- or closed-source models.
Now you want to move some of that intelligence onto the device.
But you do not know which operations belong there. You do not know which models will survive the move without degrading the application. And you do not know what optimal means once accuracy, latency, memory, power, hardware, and the shape of a user session all begin pulling in different directions.
Should you run a quantized 3B parameter model or a smaller 1.5B parameter model? Should the model remain resident in memory to minimize time to first token? Can it? What happens when the user opens another memory-intensive application, the battery begins to fall, or the session lasts longer than expected?
Even once you discover a viable on-device policy, another set of problems appears. You need to route eligible traffic onto the device, fall back to the cloud when local execution is no longer safe, and preserve the behavior of the application across both paths.
Then the ground moves again.
Traffic patterns change. New features are built. New models are released. New quantizations become available. Devices become more capable. A policy that was optimal six months ago may no longer be optimal today.
Discover, Route, Execute
Discover
An offline inference-policy search engine. It observes representative application traffic and continuously searches across models, quantizations, execution strategies, memory configurations, and device classes to determine which operations can run locally, under what conditions, and when they should fall back to the cloud.
Route
A production policy layer. For each request, it considers the operation, device state, session context, and application requirements, then chooses the appropriate on-device or cloud execution path.
Execute
The on-device runtime. It carries out the policy efficiently, managing model residency, memory, scheduling, and execution across supported hardware.
Inference placement is a moving target.
- Which operations can move on-device without changing product behavior?
- Which local model, quantization, and runtime strategy should each operation use?
- When should a model stay resident, and when should memory be released?
- How should routing react to battery, memory pressure, device class, and session shape?
- When should a previously good policy be rediscovered?