How LLMs survive in low precision | Quantization Fundamentals

3-Second Blueprint (English)

1. Replacing FP32/BF16 weights and activations with compact integers cuts VRAM footprint and multi-cycle floating-point ALU overhead.
2. Affine quantization maps real intervals to integer buckets using a scale factor and zero-point offset with controlled clipping.
3. Fixed-point integer arithmetic executes matrix multiplications directly in quantized space across PTQ and QAT workflows.