Deploy AI models at the edge for ultra-low latency without network round-trips.

Edge AI Deployment

Model Optimization for Edge

import torch
from torch.quantization import quantize_dynamic

# Dynamic quantization for edge deployment
model_fp32 = YourModel()
model_int8 = quantize_dynamic(
    model_fp32,
    {torch.nn.Linear},  # Quantize linear layers
    dtype=torch.qint8
)

# Save optimized model
torch.save(model_int8.state_dict(), "edge_model_int8.pt")
# Typically 4x smaller, 2x faster on CPU

# TorchScript for deployment
scripted_model = torch.jit.script(model_int8)
scripted_model.save("edge_model_scripted.pt")

ONNX Runtime for Cross-Platform

import onnxruntime as ort
import numpy as np

# Load ONNX model (runs on CPU, GPU, mobile, edge)
session = ort.InferenceSession(
    "model.onnx",
    providers=["CUDAExecutionProvider", "CPUExecutionProvider"]
)

# Run inference
input_name = session.get_inputs()[0].name
output = session.run(None, {input_name: input_array})

# ONNX Runtime optimizations enabled automatically:
# - Operator fusion
# - Memory optimization
# - CPU-specific vectorization

WebAssembly for Browser

// Run AI model directly in browser (no server)
import { pipeline } from '@xenova/transformers';

// Model downloads and caches in browser
const classifier = await pipeline('sentiment-analysis');
const result = await classifier('I love this product!');
// [{label: 'POSITIVE', score: 0.999}]
// Zero latency after initial download

Architecture Comparison

When to Use Edge AI

• Privacy-sensitive data (healthcare, legal)
• Offline requirements
• Consistent sub-100ms latency requirements
• High volume making cloud API cost prohibitive
• Regulatory data residency requirements