ScriptAI Infrastructurev1.2

Edge Model Optimization

by Community · open-source · Last verified 2026-03-17

Optimizes PyTorch or TensorFlow models for edge deployment by applying INT8/FP16 quantization, ONNX export, and TFLite conversion with platform-specific tuning for ARM/NPU targets. Benchmarks latency, memory, and accuracy trade-offs across optimization strategies and generates a deployment report.

https://github.com/NVIDIA/TensorRT ↗

C+

C+—Average

Adoption: BQuality: AFreshness: ACitations: C+Engagement: F

Specifications

License: Apache-2.0
Pricing: open-source
Capabilities: int8-quantization, onnx-export, tflite-conversion, benchmark-report
Integrations: onnxruntime, tensorrt, tflite, torchscript
Use Cases: mobile-ml, iot-inference, raspberry-pi-deployment
API Available: No
Language: python
Dependencies: torch, onnx, onnxruntime, tensorflow-lite-runtime, numpy
Environment: Python 3.10+
Est. Runtime: 5-30 minutes per model
Tags: edge-deployment, onnx, quantization, tflite, model-compression
Added: 2026-03-17
Completeness: 100%

Index Score

58.7

Adoption

Quality

Freshness

Citations

Engagement

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