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The original was posted on /r/machinelearning by /u/Soumil30 on 2024-11-11 23:23:31+00:00.

I’ve been diving into the world of large language models (LLMs) and have been exploring various optimization techniques. One thing that’s puzzled me is the disparity in the availability and adoption of quantization versus pruning.

Quantization seems to be a well-established and widely used technique for reducing the memory footprint and computational cost of LLMs. It’s relatively straightforward to implement and has seen significant adoption in both research and industry.

On the other hand, pruning—which involves removing less important weights from the model—is less common. Despite its potential benefits, such as further reducing model size and inference time, it doesn’t seem to be as generally available or as widely adopted. Many of my searches on the internet just result in research papers or proof of concept GitHub repos.

I’m curious about the reasons behind this disparity. Are there technical challenges with pruning that make it less practical? Is it more difficult to implement or integrate into existing workflows? Or are there other factors at play?