What On-Device AI Means
On-Device AI refers to artificial intelligence models that execute inference directly on the user's hardware — without relying on external servers or cloud-based inference APIs.
All computation happens locally. No prompts are sent externally. No outputs are logged remotely. No dependency on third-party infrastructure exists at runtime.
This is the most direct and defensible form of AI privacy.
Why On-Device AI Matters
Most AI systems marketed as "private" still rely on cloud inference. This introduces unavoidable risks: prompt and output data become data exhaust, inference can be logged or retained, behaviour depends on external policy and availability, and security posture depends on vendor assurances.
On-Device AI removes these risks by design. If intelligence never leaves the device, it cannot be observed, harvested, or repurposed.
Security Properties of On-Device AI
Running AI locally changes the threat model entirely.
Zero data egress
No network calls required
No inference logging
Nothing to intercept or retain
No platform dependency
Behaviour is deterministic and contained
Reduced attack surface
Fewer integration points
This is particularly critical in regulated or high-risk environments where exposure is unacceptable.
How On-Device AI Is Implemented
Ava Technologies deploys on-device AI using small, task-specific models optimised for local execution.
This requires model compression and optimisation, hardware-aware inference pipelines, clear capability boundaries, and predictable memory and compute profiles.
The goal is not general intelligence — it is reliable, bounded capability.
The Role of Small Models
On-device deployment is only viable with models designed for locality. Large, general-purpose models assume centralised compute, elastic scaling, and continuous connectivity.
On-Device AI favours small, specialised models that run efficiently on consumer and edge hardware, produce predictable outputs, are easier to audit and constrain, and reduce unintended behaviour.
Performance is measured per task, not by parameter count.
On-Device vs Cloud AI
The difference is architectural, not ideological.
On-Device AI
Inference runs locally
No network dependency
No external observability
Full user or organisational control
Cloud AI
Inference runs on third-party servers
Prompts and outputs leave your environment
Logging and retention are opaque
Control depends on contracts and policy
Encryption can mitigate risk, but does not remove platform dependency.
When On-Device AI Is the Right Choice
On-Device AI is particularly suited to environments where data sensitivity is high, connectivity is unreliable or restricted, regulatory exposure must be minimised, latency must be predictable, or platform dependency is unacceptable.
Common use cases include:
On-Device AI Within a Broader Architecture
On-Device AI does not require rejecting all other deployment models. In practice, it often forms the default layer in a broader system that may include self-hosted AI for controlled scale or optional encrypted compute for advanced workloads.
The key principle is simple: local execution first, external compute only when explicitly required.
Ava Technologies' On-Device Approach
Ava Technologies designs on-device AI systems that prioritise privacy by default, bounded and inspectable behaviour, predictable performance, and deployment without external dependency.
Our focus is not on maximising model size, but on maximising control.
On-device execution is the foundation of Sovereign AI.