Deployment Optimizer

Optimize models for edge deployment with quantization and pruning

Optimization Techniques

Quantization 4x smaller

Convert FP32 weights to INT8 for reduced model size and faster inference

Pruning 50% sparsity

Remove redundant connections while maintaining accuracy

Knowledge Distillation 10x smaller

Train smaller student model from larger teacher

ONNX Runtime 2x faster

Optimize for ONNX Runtime execution

Model Metrics

Original Model

Size
92.3 MB
Inference Time
15.2 ms
Accuracy
95.8%

Optimized Model

Size
23.1 MB
↓ 75% reduction
Inference Time
7.8 ms
↓ 48% faster
Accuracy
94.2%
↓ 1.6% loss

Target Deployment Platforms

Mobile
iOS/Android
Edge TPU
Coral
Jetson
NVIDIA
Raspberry Pi
ARM
Web Browser
WebAssembly
Server
x86/GPU

Optimization Pipeline

Ready