Interactive tool
Quantization Comparator — Q4 vs Q5 vs Q6 vs Q8
Pick any model, see exact VRAM at each GGUF quantization level, and compare size vs quality tradeoffs visually. Q4_K_M is the sweet spot for most users; Q5/Q6 when you have headroom; Q8 near-lossless for production workloads.
27B params · dense
We'll flag which quants fit comfortably, tight, via offload, or overflow for this VRAM budget.
Qwen 3.5 27B at 24 GB VRAM
Full model page →| Quantization | Bits | Weights | +KV cache | Total VRAM | Fit @ 24 GB | Quality |
|---|---|---|---|---|---|---|
| Q2_K | 2-bit | 10.5 GB | +1.1 GB | 11.6 GB | Comfortable | Low |
| Q3_K_S | 3-bit | 13.2 GB | +1.1 GB | 14.3 GB | Comfortable | Low |
| NVFP4 | 4-bit | 15.1 GB | +1.1 GB | 16.2 GB | Comfortable | Medium |
| Q4_K_M | 4-bit | 16.5 GB | +1.1 GB | 17.6 GB | Comfortable | Medium |
| Q5_K_M | 5-bit | 19.4 GB | +1.1 GB | 20.5 GB | Tight | High |
| Q6_K | 6-bit | 22.1 GB | +1.1 GB | 23.2 GB | Tight | High |
| Q8_0 | 8-bit | 28.9 GB | +1.1 GB | 30.0 GB | Offload | Very High |
| F16 | 16-bit | 55.4 GB | +1.1 GB | 56.5 GB | Won't fit | Maximum |
How to read: pick the highest-quality quant with fit = "Comfortable" for best output. "Tight" works but leaves no context room. "Offload" means part of the model runs on CPU memory (5-20× slower for offloaded layers). KV cache assumes default 4K-8K context; longer contexts scale it linearly.
Methodology
VRAM numbers are calibrated against real GGUF file sizes published on Hugging Face (Unsloth, bartowski, LM Studio Community). Quality labels are qualitative community consensus — Q4_K_M shows minor quality loss on chat, Q5_K_M balances quality and size, Q6_K is near-lossless, Q8_0 is effectively identical to FP16 on most tasks. For coding or structured output, prefer Q5_K_M or higher. KV cache adds 1-2 GB at default contexts; longer contexts scale KV cache linearly.
See also