Edge AI Inference: Running Small Models Blazingly Fast

Edge inference is moving from novelty to necessity. Users expect instant interactions, even offline or with flaky networks. Small, specialized models—optimized via quantization and distillation—are now capable of delivering sub-100ms responses on commodity devices.

Why edge

• Latency: perception matters; streaming partial results keeps interfaces feeling alive.
• Privacy: keep sensitive data on-device and share only aggregates.
• Cost: reduce server inference spend by handling routine tasks locally.

Techniques

• Quantization: 8-bit and 4-bit quantization shrink memory and boost throughput with minimal quality loss when calibrated well.
• Distillation: Train compact students on task-specific data to capture the essence of larger foundation models.
• Operator fusion: Merge operations to reduce memory reads and improve cache locality.
• Graph optimization: Convert to ONNX, apply kernel-level optimizations, and target GPU via WebGPU where available.

Design tips

• Split decisions: run classifiers locally, escalate complex cases to the server.
• Adaptive quality: pick model variants based on battery, thermal state, and connection.
• Telemetry with consent: collect on-device metrics to improve models without raw data exfiltration.

Edge AI is becoming a user experience feature, not just an ML choice.