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TORCHAO
Architecture optimization for PyTorch models
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ABOUT
Applying quantization, pruning, and low-precision optimizations to PyTorch models typically requires custom code and deep knowledge of hardware-specific formats. torchao provides a unified API for architecture optimization — INT4, INT8, FP8 quantization, structured pruning, and weight-only compression — that works across PyTorch models with minimal code changes, making optimization accessible without specialized hardware expertise.
INSTALL
pip install torchaoINTEGRATION GUIDE
1. Quantize PyTorch LLMs to INT8 or INT4 for 2-4x faster inference with minimal accuracy loss
2. Apply structured pruning to reduce model size and memory footprint during inference
3. Use FP8 training to reduce memory usage and speed up large model fine-tuning
4. Compress model weights with AO-specific formats for efficient deployment on edge hardware
5. Benchmark and compare optimization strategies across different PyTorch model architectures
TAGS
quantizationoptimizationpytorchfine-tuningcompressionint8