Deployment context
Device-side AI infrastructure becomes practical only when the model, runtime, and target device are considered together. Production deployment requires memory-aware graph changes, precision mapping, and hardware-specific execution planning.
ModelAdaptationQuantizationOptimizationDevice
Engineering constraints
- Static shapes reduce runtime ambiguity on constrained accelerators.
- Memory layout and KV cache behavior determine sustained inference.
- Mixed precision must be calibrated against task accuracy and device throughput.
| Constraint | Why it matters | Optimization path |
|---|---|---|
| Memory | Limits context length and batch strategy | Cache adaptation and layout planning |
| Latency | Controls product usability | Operation fusion and accelerator scheduling |
| Power | Determines sustained inference | NPU-first execution and precision mapping |