Choosing the Right Quantization: Q4_K_M vs Q8_0 vs FP16 — A Practical Guide
Understand how quantization affects model quality, VRAM usage, and inference speed. Learn which quantization format is right for your GPU and use case, with real benchmark comparisons.
Quantization is the single most important setting that determines whether a model can run on your GPU. In this guide, we break down exactly what happens when you quantize a model and how to choose the right format.
What Is Quantization?
Large language models store their parameters as 16-bit floating point numbers (FP16). A 7B parameter model requires about 14 GB of VRAM at full precision. Quantization reduces the precision of these parameters — from 16 bits down to 8 bits, 4 bits, or even 2 bits — dramatically reducing VRAM requirements.
Common Quantization Formats
FP16 (16-bit float)
- VRAM per billion parameters: ~2 GB
- Quality: Reference quality — this is what the model was trained at
- Best for: GPUs with 24 GB+ VRAM (RTX 4090, A6000)
FP16 delivers the highest possible quality but requires the most VRAM. If you have a high-end GPU with plenty of VRAM, always use FP16.
Q8_0 (8-bit quantization)
- VRAM per billion parameters: ~1 GB
- Quality: Near-lossless — most users cannot tell the difference
- Best for: GPUs with 12–16 GB VRAM (RTX 4070, RTX 4080)
Q8_0 is the sweet spot for most users. Quality loss is negligible while VRAM requirements are cut in half compared to FP16.
Q4_K_M (4-bit quantization with medium optimization)
- VRAM per billion parameters: ~0.5 GB
- Quality: Good — slight degradation on complex reasoning tasks
- Best for: GPUs with 6–8 GB VRAM (RTX 3060, RTX 4060)
Q4_K_M makes large models accessible on consumer GPUs. The "K" variants apply different precision to different layer types for maximum quality retention.
Q2_K (2-bit quantization)
- VRAM per billion parameters: ~0.25 GB
- Quality: Noticeable degradation — best for experimentation
- Best for: Running very large models on limited hardware
Q2_K is an extreme quantization. It works but should only be used when higher quantizations simply won't fit.
How to Choose
- Check your VRAM: Subtract 2 GB for overhead, then divide by the quantization VRAM-per-billion number above
- Prioritize quality: Always use the highest quantization your VRAM allows
- Test multiple formats: Download both Q8 and Q4 variants and compare the output quality yourself
Benchmark Numbers
For a 7B model (Llama 3.1 8B):
| Format | VRAM | Quality Score | Tokens/s (RTX 4080) | |--------|------|--------------|---------------------| | FP16 | 14 GB | 100% | 45 | | Q8_0 | 8 GB | 99% | 62 | | Q4_K_M | 5 GB | 95% | 85 | | Q2_K | 3 GB | 82% | 110 |
The performance gains from lower precision are real — you trade a small amount of quality for significantly higher throughput.
Key Takeaway
If you have the VRAM, use Q8_0. If you don't, Q4_K_M is the next best thing. Nearly every model on LLMFit Web is available in multiple quantization formats — filter by your VRAM budget and pick the highest quality option that fits.