AI Research Lab · Project
Small Language Models for the Edge
Capable AI that runs directly on-device — no cloud required.
Overview
Not every problem needs a giant model. We build small, efficient language models that run directly on edge devices — phones, IoT gateways, and industrial controllers — with low latency, low power, and full privacy because data never leaves the device.
Objectives
- On-device / offlineRuns without a network connection.
- Low latencyInstant responses at the edge.
- Low footprintSmall memory and power budget.
- Private by designData stays local to the device.
Our Approach
- DistillationCompress knowledge from larger teacher models.
- QuantizationINT8 / INT4 for smaller, faster models.
- Pruning & sparsityRemove redundant parameters without losing quality.
- Hardware-aware designEfficient architectures tuned to target devices.
Focus & Tech
DistillationQuantization (INT8/INT4)PruningEdge DeploymentOn-device Inference
Status: Prototyping efficient training
Interested in this work?
We collaborate on efficient, on-device AI.