Lesson 513 of 1596
Quantization Choices: FP16, Q8, Q6, Q5, and Q4
Quantization is the art of making models fit local hardware by using fewer bits, while watching how quality changes.
Creators · Model Families · ~13 min read
The operational idea: quantization choices
Quantization is the art of making models fit local hardware by using fewer bits, while watching how quality changes. In local AI, the model family is only one part of the system. The runtime, file format, serving path, hardware budget, evaluation set, and safety policy decide whether the model becomes useful.
Compare the options
| Layer | What to decide | What can go wrong |
|---|---|---|
| Runtime | quantization choices | The model runs, but the workflow is slow or brittle |
| Evaluation | A small task-specific test set | A flashy demo hides routine failures |
| Safety and ops | Permissions, provenance, logging, and rollback | Choosing the smallest file because it loads, then discovering the model fails the actual task. |
Current source signal
Build the small version
Run the same model family at two quantization levels and score speed, memory use, and answer quality.
- 1Define the user task in one sentence.
- 2Choose the smallest model and runtime that might pass that task.
- 3Run one happy-path prompt and one failure-path prompt.
- 4Record speed, memory pressure, output quality, and the exact reason for any failure.
- 5Write the operating rule you would give a non-expert user.
A local-model operations sketch students can adapt.
quantization_scorecard: model: same-family-same-size variants: [FP16, Q8, Q4] measure: - disk_size - load_memory - tokens_per_second - format_following - task_accuracy choose: smallest variant that passes the rubricKey terms in this lesson
The big idea: smallest passing quant. A local model app is not done when the model answers once; it is done when the whole workflow can be installed, measured, trusted, and recovered.
End-of-lesson quiz
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