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When you're fine-tuning a neural network, the trick is to squeeze every last drop of performance without letting it choke on its own complexity. It's not just about making code run faster; it's a delicate dance between model accuracy and computational cost. Techniques like pruning away redundant parameters or quantizing weights down to smaller data types can drastically shrink a model's footprint. By carefully managing these trade-offs, you can make a model light enough to run on a smartphone w…

[https://wiki-global.win/index.php/Why_AMD_CPU_GPU_Integration_Matters_More_Than_Ever_for_Modern_Computing ai performance optimization 7]