How LLM Quantization Works: Shrinking Model Size from FP16 to INT4

Quantization compresses model weights to lower precision so large language models can fit in less memory and run faster, and it is widely used in production AI from on-device models to large inference clusters. The explanation works from the ground up, starting with why a 70B-parameter model stored in FP16 or BF16 needs roughly 140 GB, then walking through how values are mapped to smaller ranges layer by layer.

■ Why Quantization Matters
- How weights and activations determine a model's memory footprint
- Storage math comparing FP16, FP8/INT8, and INT4 for a 70B-parameter model
- Precision suffixes seen in model names

■ How Quantization Works
- Mapping a layer's full value range onto a smaller set and rounding
- Standard formats: FP16 (2 bytes), FP8/INT8 (1 byte), INT4 (0.5 bytes)
- Why values cannot simply be truncated bit by bit

■ Trade-offs and Methods
- Accuracy impact at INT8 versus the more aggressive INT4
- Post-training quantization (PTQ) versus quantization-aware training (QAT)

This is aimed at ML engineers, practitioners, and anyone deploying models under memory or latency constraints who wants a clear mental model of precision trade-offs. Viewers come away able to read precision tags in model names, weigh PTQ against QAT, and decide how far to push quantization for their own deployment.