A Short Guide to Deploying Models on Constrained Edge Devices

Quantization, batching, and the memory tricks that got inference running on cheap edge hardware.

Start with the budget

Fix the memory and latency budget first, then choose the model. Not the other way around.

Quantize and measure

Post-training dynamic quantization is usually the cheapest win:

import torch

# dynamic INT8 quantization for CPU / edge inference
quantized = torch.quantization.quantize_dynamic(
    model, {torch.nn.Linear}, dtype=torch.qint8
)
torch.save(quantized.state_dict(), "model.int8.pt")Code language: Python (python)

Then squeeze the runtime

• Fuse conv + batch-norm layers
• Batch opportunistically, cap the queue
• Pin buffers and reuse them across frames