Q4 vs Q8 Quality Loss in Ollama: Practical Decision Guide（英文原文）
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Most users asking about Q4 vs Q8 are not asking a research question. They are making a deployment decision under VRAM constraints.
The practical rule
- If your workflow is interactive chat, coding assistance, and short answers, Q4 is usually enough.
- If your workflow needs stable factual extraction, strict formatting, or high-stakes summarization, Q8 is safer.
Why quality drops in Q4
Quantization compresses weights. Q4 reduces memory pressure, but that compression can reduce output stability, especially with long outputs.
Where Q4 performs well
- 7B to 14B chat models
- Fast iteration and prototyping
- RAG pipelines where retrieval quality is strong
Where Q8 has clear value
- Long reasoning chains
- Precise extraction tasks
- Reproducibility-sensitive enterprise workflows
LocalVRAM recommendation
Use Q4 as default for fit and speed, then run blind comparison on your real prompts before promoting to production.
If Q4 fails consistency checks, move to Q5/Q6 first before jumping straight to Q8.