AzuroNanoOpt v6.1 is a next-gen AI optimization engine built for edge devices, micro-GPUs, NPUs and embedded ML workflows.
Designed for extreme efficiency, fast convergence and seamless deployment.
Training Performance
Dataset: 2,000 train / 500 test
Accuracy: 100% by epoch 6, stable through epoch 10
Loss: 2.305 → 0.038 (adaptive LR: 0.01 → 0.00512)
Stability: Clean convergence even on very small datasets
Speed & Efficiency
Step time: 4.28 ms
Throughput: 25.56M params/sec
Inference latency: 2.36 ms → 2.34 ms (INT8)
Hardware: Standard CPU (no GPU acceleration)
Model Compression
Original: 0.42 MB
INT8 Quantized: 0.13 MB
70% reduction with 0% accuracy loss
MSE: 0.00000000, max diff: 0.000000
ONNX Export (Opset 18)
Dynamic shapes
File size: 0.01 MB
Fully clean graph, zero warnings
Production-ready on Windows, Linux, embedded boards
Licensing
30-day fully functional trial
Enterprise-grade isolation & sandbox support
mail: [email protected]
AzuroNanoOpt v6.1 is a next-gen AI optimization engine built for edge devices, micro-GPUs, NPUs and embedded ML workflows. Designed for extreme efficiency, fast convergence and seamless deployment.
Training Performance
Dataset: 2,000 train / 500 test
Accuracy: 100% by epoch 6, stable through epoch 10
Loss: 2.305 → 0.038 (adaptive LR: 0.01 → 0.00512)
Stability: Clean convergence even on very small datasets
Speed & Efficiency
Step time: 4.28 ms
Throughput: 25.56M params/sec
Inference latency: 2.36 ms → 2.34 ms (INT8)
Hardware: Standard CPU (no GPU acceleration)
Model Compression
Original: 0.42 MB
INT8 Quantized: 0.13 MB
70% reduction with 0% accuracy loss
MSE: 0.00000000, max diff: 0.000000
ONNX Export (Opset 18)
Dynamic shapes
File size: 0.01 MB
Fully clean graph, zero warnings
Production-ready on Windows, Linux, embedded boards
Licensing
30-day fully functional trial
Enterprise-grade isolation & sandbox support mail: [email protected]
Demo: pip install azuronanoopt-kr TestPyPI: azuronanoopt-kr