Quantization Guide
Choose the right quantization without guesswork
- Quantization is the primary lever for fitting larger models on consumer GPUs
- Q4/Q4_K_M is the practical default for most local AI users
- Q5_K_M is often the best upgrade path when Q4 quality is insufficient
- Use quantization-specific requirements and speed pages, not generic assumptions
- Choose a format with memory headroom, not just minimum fit
What Is Quantization?
Quantization compresses model weights so you can run larger models on smaller GPUs. You trade some precision for lower memory usage and often faster inference.
Why It Matters for Local AI
Without quantization, many popular models exceed consumer GPU VRAM. Quantized formats make local inference practical.
The Core Tradeoff
Lower-bit formats reduce VRAM and may increase speed, but they can reduce output fidelity on more complex reasoning tasks.
Common Quantization Formats
Different formats target different quality/speed/VRAM goals.
Q4 and Q4_K_M
Great starting point for most users. Broad compatibility, low VRAM footprint, and usually acceptable quality for chat and coding assistance.
Q5_K_M
Middle ground between Q4 and Q8. Useful when Q4 quality feels weak and you still need tighter VRAM use than Q8.
Q8 and FP16
Higher precision, generally better fidelity, and higher memory requirements. Good for users with strong hardware and quality-first workloads.
VRAM and Quality Tradeoffs
Use requirements pages and compatibility pages together: requirements estimate fit, compatibility verifies whether a given GPU can run it comfortably.
What to Check First
Start with model Q4 requirements, then inspect Q5_K_M/Q8 if your GPU has headroom. Check quantization-level speed and verdicts, not just minimum fit.
Quality Expectations
The perceived quality gap depends on task type. Routine chat and extraction are often tolerant; nuanced reasoning and long-form outputs are less tolerant.
How to Choose for Your GPU
Choose the highest precision that fits with operational headroom rather than the absolute minimum that barely loads.
Beginner Rule
If unsure, start on Q4 or Q4_K_M. If quality is insufficient and you have VRAM headroom, test Q5_K_M, then Q8.
Production Rule
Standardize one quantization per workload, benchmark latency and quality, and keep a fallback profile for lower-memory hardware.
Common Mistakes
Most issues come from mismatched expectations or ignoring quantization-specific data.
Ignoring Headroom
Running exactly at VRAM limit can lead to unstable behavior. Prefer configurations with additional memory margin.
Comparing Different Prompt Settings
When comparing quantizations, keep temperature, context, and prompt style consistent to avoid false conclusions.
Frequently Asked Questions
Related Guides & Resources
Ready to Get Started?
Check our step-by-step setup guides and GPU recommendations.