Pruning

Pruning removes unnecessary connections or parameters from a neural network, shrinking model size and accelerating inference while preserving most accuracy. Common approaches include magnitude pruning (remove weights closest to zero), structured pruning (remove entire neurons, filters, or attention heads to enable real speedup), and unstructured pruning (remove individual weights, which mainly saves storage). Famous pruning research includes Han et al.'s Deep Compression work that combined pruning, quantization, and Huffman coding to shrink AlexNet 35x without accuracy loss. Modern LLM pruning techniques include SparseGPT, Wanda, and LLM-Pruner — capable of pruning 50% of a model's parameters with minimal performance degradation. The combination of pruning, quantization, and distillation often achieves 10-100x compression with acceptable quality. AI governance, AI compliance, and AI risk management programs document compression techniques in model cards supporting responsible AI reproducibility across optimized enterprise AI deployments worldwide.

Centralpoint Governs Pruned and Full Models Identically: Whether you serve a pruned Llama variant or a full-precision frontier model, Centralpoint by Oxcyon tracks every interaction across OpenAI, Gemini, Llama, and embedded options. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds chatbots into your portals via one JavaScript line.


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