AI Model Compression: Quantization, Pruning, Distillation, and Deployment

Expert guide to AI model compression in 2026: weight quantization (GPTQ, AWQ, GGUF, bitsandbytes), structural pruning (SparseGPT, Wanda), knowledge… From the article "AI Model Compression: Quantization, Pruning, Distillation, and Deployment" by Synor, published on Misar.Blog. In this episode: 0:00 Introduction 0:13 Model Compression Reduces LLM Size and Inference Cost by 2-10x 0:40 Quantization 1:01 Pruning—removing Redundant Weights or Attention Heads 1:29 Knowledge Distillation Trains a Smaller “Student” Model to Replicate 2:04 MoEfication Converts Dense Models to Mixture-Of-Experts Architecture 2:56 Quantized Llama 3 70B at INT4 Runs 2.5x Faster and Uses 40GB Instead of 140GB 4:03 INT4 Quantization Reduces Memory Bandwidth Needs and Inference Cost 4:39 Weight-Only Quantization Compresses Weights to 4-8 Bits 5:17 SparseGPT Achieves One-Shot Weight Pruning with Compensation This episode is narrated by an AI voice from a written article. More episodes: Tokenized Money Market Funds vs Stablecoins: On-Chain Cash Best PSU for a Used RTX 4090 (2026 Picks) Best Embedding Models 2026: OpenAI vs Voyage vs Open-Source Related reads: RTX 5090 Power Supply Guide: Wattage, Connectors, PSU Picks How to Prepare a Dataset for RLHF and DPO Fine-Tuning 🔔 Subscribe to every episode Read the article: https://www.misar.blog/@synor/articles/ai-model-compression. Read the articles by this Author: https://www.misar.blog/@synor. Generated using: https://www.misar.ai (Misar.AI).