Nexa SDK

Developers typically use Nexa SDK by packaging a model with an app and running inference directly on the user’s machine. A common workflow is to choose a target device, let the runtime pick the best available accelerator, and then route requests through a local API endpoint so the rest of the application behaves like it would with a hosted service. This makes it easy to keep features working when the network is slow or unavailable and to avoid sending sensitive data off-device.

In practice, teams plug it into assistants and agents that need fast responses and predictable costs. The local server can stream tokens to the UI while the app simultaneously triggers structured actions using schema-driven tool calls, such as searching local files, filling forms, or controlling device features. For multimodal apps, the same setup can accept text prompts alongside images or audio, enabling flows like voice dictation to text, reading responses aloud, describing what the camera sees, or generating images for creative tools.

Deployment often involves testing the same code path across laptops, desktops, and edge devices, then tuning speed and memory by selecting an appropriate model format and quantization level. Many teams start by pulling a model from Hugging Face, validate it locally, and later switch to a more optimized format for production builds to reduce latency and footprint while keeping output quality within their requirements.