domains / families / research / model-pruning

model-pruning

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Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.

When to use

• **Reduce model size** by 40-60% with <1% accuracy loss
• **Accelerate inference** using hardware-friendly sparsity (2-4× speedup)
• **Deploy on constrained hardware** (mobile, edge devices)
• **Compress without retraining** using one-shot methods
• **Enable efficient serving** with reduced memory footprint

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