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Q4 vs Q6 Quantization for Local LLMs: Which Should You Choose?

Cowrie
Cowrie
Dev @ Bswen

Problem

When I started running local LLMs, I was confused about quantization. I saw Q4, Q5, Q6, Q8 options and didn’t know which to pick.

The question was: Should I sacrifice quality to save VRAM, or use higher quantization and risk running out of memory?

The Short Answer

Choose Q4 when VRAM is constrained or you need larger context windows. Choose Q6 when quality is paramount and you have sufficient VRAM headroom.

For RTX 5090 with 32GB VRAM, Q6 works for models up to ~30B parameters. Q4 lets you run larger models or leave more VRAM for context.

What Quantization Does

Quantization compresses model weights from 16-bit or 32-bit floats to fewer bits:

Quantization size comparison
| Format | Bits per weight | Model size (30B params) |
|--------|-----------------|-------------------------|
| FP16   | 16              | ~60 GB                  |
| Q8     | 8               | ~30 GB                  |
| Q6     | 6               | ~22.5 GB                |
| Q5     | 5               | ~18.75 GB               |
| Q4     | 4               | ~15 GB                  |

Lower bits = smaller model = less VRAM, but some quality loss.

VRAM Estimation

I use this formula to check if a model fits:

VRAM estimation function
def estimate_vram(params_billion: float, quant_bits: int, context_tokens: int) -> float:
    """
    Estimate VRAM requirement in GB.


    params_billion: Model parameters in billions (e.g., 30 for 30B)
    quant_bits: Quantization bits (4, 6, 8)
    context_tokens: Maximum context window length
    """
    model_vram = (params_billion * quant_bits) / 8
    # KV cache: approximately 2 bytes per parameter per token
    kv_cache = (params_billion * 2 * context_tokens) / (8 * 1024)
    return model_vram + kv_cache


# Q4_K_M 30B with 8K context
print(f"Q4 30B, 8K context: {estimate_vram(30, 4, 8192):.1f} GB")
# Output: ~16.6 GB (fits 32GB GPU with headroom)


# Q6_K 30B with 8K context
print(f"Q6 30B, 8K context: {estimate_vram(30, 6, 8192):.1f} GB")
# Output: ~24.1 GB (tighter fit on 32GB)

Real-World Trade-offs

One Reddit user mentioned they “run Qwen3.5 27B at Q4 to Q6 depending on what I want for my context window.” This shows the choice is situational.

When Q4 Makes Sense

  • Limited VRAM (16GB-24GB GPUs)
  • Need long context windows
  • Running larger models (30B+) on single GPU
  • Speed is important (smaller model = faster inference)

When Q6 Makes Sense

  • Ample VRAM (32GB+ GPUs)
  • Quality-critical applications
  • Working with smaller models (7B-14B)
  • Maximum accuracy needed

Quality Difference

Is Q4 really worse? In my testing, modern Q4_K_M format is often indistinguishable from Q6 for most tasks.

The key insight from the community: “There’s a direct trade-off between context window size and model quality based on quantization level.”

RTX 5090 32GB decision guide
┌─────────────────────────────────────────────────────────────┐
│                    Your Use Case                            │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Need long context (32K+) ──────────────────────► Q4       │
│                                                             │
│  Running 30B+ model ─────────────────────────────► Q4       │
│                                                             │
│  Maximum quality, 7B-14B model ──────────────────► Q6       │
│                                                             │
│  Quality critical, have headroom ────────────────► Q6       │
│                                                             │
└─────────────────────────────────────────────────────────────┘

How to Choose Quantization

Step-by-step decision process:

  1. Check your VRAM - How much do you have?

  2. Pick your model size - Which model do you want to run?

  3. Calculate base VRAM - Use the formula above

  4. Factor in context - How much context do you need?

  5. Test both - Quality differences are often use-case dependent

Common Mistakes

  1. Assuming Q4 is “bad” - Modern Q4_K_M preserves 90%+ of model quality. It’s not the same as old Q4 formats.

  2. Ignoring context window VRAM - Context needs scale with sequence length. A 65K context window needs significant VRAM.

  3. Not testing on your specific use case - Quality differences vary by model and task type. Code generation might show different results than general chat.

  4. Over-quantizing large models - A heavily quantized 100B model often underperforms vs. a well-quantized 30B model.

Practical Example

For my RTX 5090 with 32GB VRAM running Qwen3-coder-30B:

Q4 vs Q6 comparison for 30B model
| Metric           | Q4_K_M    | Q6_K      |
|------------------|-----------|-----------|
| Base model VRAM  | ~15 GB    | ~22.5 GB  |
| Context (8K)     | ~1.6 GB   | ~1.6 GB   |
| Total estimate   | ~16.6 GB  | ~24.1 GB  |
| Remaining VRAM   | ~15.4 GB  | ~7.9 GB   |
| Context headroom | Plenty    | Limited   |

With Q4, I have room for 32K+ context. With Q6, I’m more limited.

Summary

In this post, I compared Q4 vs Q6 quantization for local LLMs. The key point is Q4 is the practical choice for VRAM efficiency and long context, while Q6 suits quality-sensitive applications with ample GPU memory.

For RTX 5090 owners, Q4_K_M is the sweet spot for 30B+ models, balancing quality and flexibility. You can always test both on your specific workload.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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