Model Pruning

Model pruning is the family of model-compression techniques that remove weights, neurons, attention heads, or entire layers from a trained neural network to reduce its size and inference cost while preserving as much accuracy as possible. Pruning has a long history (the foundational work goes back to Optimal Brain Damage, LeCun et al. 1990) and has become urgent for LLMs at billion-parameter scale where deployment cost dominates training cost. The taxonomy: unstructured pruning (set individual weights to zero, producing sparse matrices that require specialized hardware support to actually speed up — NVIDIA's 2:4 sparsity on Ampere and Hopper GPUs is the dominant such format), structured pruning (remove entire rows, columns, attention heads, or layers, producing a smaller dense model that runs faster on standard hardware), and semi-structured pruning (sparse blocks rather than individual weights). The major LLM-pruning techniques include Wanda (Sun et al. 2023, prune based on weight magnitudes weighted by input activations), SparseGPT (Frantar and Alistarh 2023, one-shot pruning to 50%+ sparsity in hours rather than days), LLM-Pruner (Ma et al. 2023, structured pruning with importance estimation), Sheared Llama (Xia et al. 2023, prune and continue pretraining to recover quality), and depth pruning (drop entire transformer layers, surprisingly effective in some models). Production pruning recipes typically achieve 30-50% parameter reduction with 1-3 percentage point accuracy drops on standard benchmarks; combining pruning with quantization (AWQ, GPTQ) and knowledge distillation compounds the gains. The practical caveat: pruning quality varies enormously across tasks and models — generic benchmarks may improve while specific applications regress, so post-pruning evaluation on the actual deployment task is essential. AI governance teams document pruning lineage (base model, pruning technique, retained sparsity level, recovery training if any) in the model registry because the pruned model is a different artifact with different operational characteristics from the original.

Compression discipline from 25 years of content optimization: Centralpoint has compressed, optimized, and stream-served content across bandwidth-constrained environments for 25 years — model pruning is the same optimization mindset applied to a new artifact type. Pruning runs on-premise, tokens meter per skill, and pruned-model chatbots deploy through one line of JavaScript.


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