LLM quantization is how a 70B model that needs 140GB of memory gets small enough to run on a normal GPU.
Every model you chat with is really a giant file of numbers, and those numbers have to fit in memory somewhere. Quantization stores each number in fewer bits so the file shrinks. In this video we untangle the three things people constantly mix up: number formats, quantization methods, and file formats. Then we show why dropping precision is never free.
? What you'll learn:
1️⃣ Why a 70B model needs roughly 140GB, and where that number actually comes from
2️⃣ What FP16, BF16, FP8, INT8, and INT4 mean for size and precision
3️⃣ How GPTQ and AWQ shrink a model without wrecking its output
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⏰ Timestamps:
00:00 - Where LLMs actually live
00:39 - Why a 70B model needs 140GB
01:13 - What is Quantization?
01:56 - 3 Ways Quantization is done
03:08 - Who should quantize a model?
03:33 - The trade-off: smaller but still useful
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