On-device AI refers to running AI models directly on a local device, a phone, laptop, or edge server, rather than sending data to a remote cloud server for inference.
On-device AI requires models small and efficient enough to run within a device's memory and compute constraints, which is why it's closely tied to techniques like model quantization and distillation that shrink a model's footprint with minimal quality loss. In exchange for that constraint, on-device inference offers three major advantages: privacy, sensitive data never leaves the device, latency, no network round-trip to a remote server, and offline availability, the model works without an internet connection.
The tradeoff is capability: even a well-optimized on-device model is typically smaller and less capable than a frontier cloud-hosted model with far more parameters and compute behind it. Many real-world systems use a hybrid approach, handling simple, latency-sensitive, or privacy-sensitive tasks on-device while routing complex reasoning tasks to a larger cloud model, giving users the best of both depending on the task at hand.
Neotask's model routing can prefer a smaller on-device or self-hosted model for a tenant's latency-sensitive or data-residency-restricted steps, while still calling out to a larger frontier model for steps that need deeper reasoning, all within the same agent workflow.
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