Definition
Quantization is the process of mapping high-precision floating-point weights and activations (e.g., FP32 or FP16) to lower-precision formats (e.g., INT8 or INT4) to minimize memory footprint and accelerate inference in AI agents. This architectural trade-off significantly reduces VRAM requirements and latency at the cost of a marginal increase in model perplexity or reasoning errors.
Focuses on numerical precision reduction for model compression, not the volume of data points.
"Downsampling a high-resolution 4K video to 1080p to allow it to stream smoothly on a slower connection while maintaining visual clarity."
- FP16/BF16(Prerequisite)
- Perplexity(Performance Metric)
- Product Quantization (PQ)(Component for Vector DB Indexing)
- VRAM Bottleneck(Problem solved)
Conceptual Overview
Quantization is the process of mapping high-precision floating-point weights and activations (e.g., FP32 or FP16) to lower-precision formats (e.g., INT8 or INT4) to minimize memory footprint and accelerate inference in AI agents. This architectural trade-off significantly reduces VRAM requirements and latency at the cost of a marginal increase in model perplexity or reasoning errors.
Disambiguation
Focuses on numerical precision reduction for model compression, not the volume of data points.
Visual Analog
Downsampling a high-resolution 4K video to 1080p to allow it to stream smoothly on a slower connection while maintaining visual clarity.