Model Pruning

Unstructured pruning removes individual weights, while structured pruning removes units such as channels or other blocks. Selection can use magnitude or another importance criterion, and subsequent fine-tuning may recover some lost quality. A mask can leave the original dense tensor shape unchanged, so storage and execution savings depend on representation and kernel support. Pruning changes model structure or effective connectivity and should be distinguished from quantization, which changes numeric precision, and distillation, which trains another model. Competence includes linking the pruning method to the actual runtime rather than treating a sparsity percentage as a complete measure of compression or efficiency.

Establish a quality and latency baseline on the target workload. Choose structured or unstructured pruning based on supported execution paths, apply it incrementally and inspect which layers are sensitive. If fine-tuning follows, preserve the same evaluation boundary and check important slices for regressions. Export the intended representation and profile the deployed artifact, including load time and memory. The deliverable should report quality and measured resource changes together, making clear whether savings arise from reduced tensors, sparse storage or specialized execution rather than from masked zeros alone.

Pruning can damage rare behaviors or layers whose importance is not captured by a simple criterion. Fine-tuning may overfit or fail to recover quality. Sparse formats and kernels have hardware-dependent overhead, and masks can retain dense storage. A headline sparsity value does not establish speedup. Pruning differs from quantization and distillation, although methods can be combined. Evaluate the final artifact's task quality and execution path rather than equating removed weights with equivalent reductions in cost.