--- title: Efficient LLM Compression with SparseGPT and Wanda on... description: LLM inference costs scale with model size, making compression critical. This deep dive covers two post-training pruning methods — SparseGPT and Wanda — that... canonical: https://daily.dev/posts/efficient-llm-compression-with-sparsegpt-and-wanda-on-gpu-cloud-60evbey47 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Efficient LLM Compression with SparseGPT and Wanda on GPU Cloud | daily.dev og:description: LLM inference costs scale with model size, making compression critical. This deep dive covers two post-training pruning methods — SparseGPT and Wanda — that... og:url: https://daily.dev/posts/efficient-llm-compression-with-sparsegpt-and-wanda-on-gpu-cloud-60evbey47 og:image: https://api.daily.dev/og/posts/60EVbEy47.png og:image:alt: Efficient LLM Compression with SparseGPT and Wanda on GPU Cloud og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Efficient LLM Compression with SparseGPT and Wanda on GPU Cloud **[DigitalOcean Community](https://daily.dev/sources/do_community)** · 14 min read · 1 upvotes · 0 comments ## Summary LLM inference costs scale with model size, making compression critical. This deep dive covers two post-training pruning methods — SparseGPT and Wanda — that reduce model weight counts without retraining. SparseGPT uses second-order reconstruction to minimize layer output error after pruning, while Wanda uses a simpler activation-aware importance score (weight magnitude × input activation norm). A comparison table and PyTorch code example for Wanda pruning are included. Key caveats: sparsity alone doesn't guarantee speedups — the inference stack must support sparse formats and kernels, and hardware must be compatible (e.g., NVIDIA Ampere 2:4 sparsity). SparseGPT is preferred for high-sparsity accuracy retention; Wanda for fast experimentation. Production deployments typically combine pruning with quantization, KV-cache management, and batching strategies. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [The LLM Inference Optimization Stack: From Quantization to Speculative Decoding Part 1](https://daily.dev/posts/the-llm-inference-optimization-stack-from-quantization-to-speculative-decoding-part-1-ryexzxqsr) · DigitalOcean Community · 0 upvotes · 0 comments - [Accelerating LLM inference with post-training weight and activation using AWQ and GPTQ on Amazon SageMaker AI](https://daily.dev/posts/accelerating-llm-inference-with-post-training-weight-and-activation-using-awq-and-gptq-on-amazon-sag-jbmt9237k) · AWS · 3 upvotes · 0 comments - [Pruning and Distilling LLMs Using NVIDIA TensorRT Model Optimizer](https://daily.dev/posts/pruning-and-distilling-llms-using-nvidia-tensorrt-model-optimizer-ujki8fjpw) · NVIDIA Developer · 3 upvotes · 0 comments --- Tags: [#pytorch](https://daily.dev/tags/pytorch) [View this post on daily.dev](https://daily.dev/posts/efficient-llm-compression-with-sparsegpt-and-wanda-on-gpu-cloud-60evbey47) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Efficient LLM Compression with SparseGPT and Wanda on GPU Cloud","url":"https://daily.dev/posts/efficient-llm-compression-with-sparsegpt-and-wanda-on-gpu-cloud-60evbey47","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/efficient-llm-compression-with-sparsegpt-and-wanda-on-gpu-cloud-60evbey47"},"datePublished":"2026-06-19T13:19:52.808Z","dateModified":"2026-06-19T13:20:48.729Z","description":"LLM inference costs scale with model size, making compression critical. 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