Technology
Our on-device inference stack focuses on practical performance: latency, memory footprint, energy, and reliability.
Model Optimization
- Quantization (int8/int4), mixed precision
- Pruning & distillation for edge budgets
- Operator fusion / graph optimization
Runtime & Acceleration
- Device-specific kernels and scheduling
- CPU/GPU/NPU backend strategy
- Stable streaming for multimodal IO
Evaluation & Shipping
- Latency / memory / battery profiling
- Quality & alignment checks for multimodal outputs
- Release pipeline: artifacts, versioning, rollback