A quantized model is a machine-learning model whose numeric weights are stored at reduced precision — for example 8-bit integers in place of 32-bit floating point — to cut memory use and computation cost, usually in exchange for a small, measurable drop in accuracy.
| Term | Quantized Model |
|---|---|
| Category | APIs, Reliability and Publishing |
| Also known as | Quantized Embedding Model |
| Where it appears | Semantic enrichment evaluator |
What it means in RankGear
The semantic enrichment evaluator turns page text into embedding vectors so it can measure how closely content matches a topic. When that embedding model is quantized, its weights are held at lower numeric precision, which shrinks the model in memory and speeds up each pass. That trade matters most when a workbook covers hundreds of URLs at once: the quantized model keeps the evaluation running quickly and within a modest memory footprint. The vectors it produces are very close to — but not bit-for-bit identical to — those a full-precision model would return.
How to interpret it
Read quantization as a speed-and-footprint choice, not a quality verdict. Within a single run the scores are internally consistent, so ranking pages against one another by semantic relevance stays dependable. What you should not do is compare absolute values across runs made at different precisions, because a quantized model and a full-precision model can put the same page at slightly different points on the scale. If numbers shift a little between an older run and a newer one, check whether the underlying model precision changed before reading meaning into the difference.
| Aspect | Full precision (float32) | Quantized (int8) |
|---|---|---|
| Memory footprint | Largest | Substantially smaller |
| Evaluation speed | Baseline | Faster |
| Accuracy | Reference | Small, measurable tradeoff |
Example
You run the semantic enrichment evaluator across 400 category and article pages to see which ones drift off their target topic. With the quantized embedding model, the batch fits comfortably in memory and finishes in a fraction of the time a full-precision run would take. The pages it flags as least relevant come out in essentially the same order as a full-precision check would produce — the practical conclusion is unchanged, even though the individual similarity figures differ by a hair.
Important considerations
- Quantization lowers precision on purpose; expect a small accuracy tradeoff rather than identical output to a full-precision model.
- Absolute scores are only comparable within runs that used the same model precision — do not compare raw values across a quantized and a non-quantized run.
- Semantic similarity is a comparative, relative indicator of topical closeness, not a Google ranking score, and a higher figure does not by itself make a page rank.
Related terms
Part of the RankGear glossary · how RankGear measures · the 870 factors.