Learn more about Quantization here → https://ibm.biz/~NdqpU881V
Huge AI models come with huge memory requirements.Grace Ableidinger explains how quantization compresses LLMs using lower-precision formats like INT8 and INT4. Learn how model size, GPU memory, speed, and accuracy trade off during AI inference.
00:00 – Understand What Quantization Does
00:23 – See Why LLMs Need Quantization
00:51 – Connect AI Parameters to Memory Requirements
01:31 – Calculate the Memory Cost of a 70B LLM
02:13 – See How Quantization Reduces Precision
02:53 – Follow Quantization Layer by Layer
04:21 – Compare FP16, FP8, INT8 & INT4
05:34 – Balance Memory Savings with Model Accuracy
06:33 – Compare PTQ vs Quantization-Aware Training
07:17 – Choose the Right Quantization Tradeoff
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#llm #aimodels #aiinference #machinelearning #aiengineering
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