Quantization Blind Test: Q4 vs Q8 Decision Workflow
This tool page is designed for practical model deployment decisions. Run one blind pass before you decide between lower VRAM footprint (Q4) and higher quality retention (Q8).
Fast Start Checklist
- Pick one real task prompt set (coding, extraction, summarization, or customer support).
- Generate two outputs: one with Q4, one with Q8. Keep temperature and prompt identical.
- Hide model labels and score quality first, then reveal labels.
Scoring Rubric (Simple and Repeatable)
| Dimension | How to score | Decision signal |
|---|---|---|
| Factual accuracy | Count factual errors or unsupported claims per output. | If Q4 error rate rises, keep Q8 for this task. |
| Instruction fidelity | Check whether required format and constraints are followed. | If Q4 misses structure repeatedly, prefer Q8. |
| Latency / throughput | Compare average tokens/s and response wait time. | If quality is close, prefer faster profile. |
| Operational cost | Estimate local VRAM pressure and cloud spillover hours. | Use lower-cost profile when quality delta is not material. |
Suggested Decision Rule
Use Q4 as default only when it passes your prompt set with no material quality loss. If the workload is accuracy-sensitive (analysis, compliance, code generation), use Q8 for primary path and reserve Q4 for fallback or low-priority traffic.
- Daily limit guideline: 5 controlled blind runs per model/task pair.
- Use one reviewer checklist across teams to avoid ad-hoc scoring drift.
- Record final decision with benchmark link and prompt snapshot.