Best Qwen3.5 4B quant for your GPU
8 quantized files of Qwen/Qwen3.5-4B (4.21B), each diffed against the BF16 original.
Qwen/Qwen3.5-4B (Apache-2.0). Same hybrid design as the 9B (8 of 32 layers full attention). Text-only GGUF quants. Tool calls tested with thinking disabled.
Recommendation by VRAM
"Best" = smallest diff (lowest mean KLD) among files that fit fully on the GPU (file + 8192-token KV cache), with tool-call score as tie-breaker.
8GB GPU
- File size
- 4.17 GB
- KLD (mean)
- 0.0019
- Same top token
- 98.3%
- Tool calls
- 86.5%
- Generation
- 114 t/s full GPU
Fastest that fits: UD-Q2_K_XL (unsloth, 187 t/s, KLD 0.2425)
12GB GPU
- File size
- 4.17 GB
- KLD (mean)
- 0.0019
- Same top token
- 98.3%
- Tool calls
- 86.5%
- Generation
- 114 t/s full GPU
Fastest that fits: UD-Q2_K_XL (unsloth, 187 t/s, KLD 0.2425)
16GB GPU
- File size
- 4.17 GB
- KLD (mean)
- 0.0019
- Same top token
- 98.3%
- Tool calls
- 86.5%
- Generation
- 114 t/s full GPU
Fastest that fits: UD-Q2_K_XL (unsloth, 187 t/s, KLD 0.2425)
All Qwen3.5 4B quants
| Quant | Publisher | Size | Fits | KLD mean | Same top | Tool calls | Gen t/s | Run |
|---|---|---|---|---|---|---|---|---|
| Q8_0 | unsloth | 4.17 GB | 8GB+ | 0.0019 | 98.3% | 86.5% | 114 | settings & logs |
| Q6_K | bartowski imatrix | 3.54 GB | 8GB+ | 0.0037 | 97.1% | 86.5% | 130 | settings & logs |
| UD-Q4_K_XL | unsloth imatrix | 2.71 GB | 8GB+ | 0.0220 | 93.3% | 85.0% | 151 | settings & logs |
| Q4_K_M | bartowski imatrix | 2.81 GB | 8GB+ | 0.0258 | 92.8% | 86.5% | 152 | settings & logs |
| Q4_K_M | unsloth | 2.55 GB | 8GB+ | 0.0308 | 92.4% | 87.0% | 156 | settings & logs |
| i1-Q4_K_M | mradermacher imatrix | 2.52 GB | 8GB+ | 0.0339 | 91.8% | 89.5% | 158 | settings & logs |
| IQ3_M | bartowski imatrix | 2.31 GB | 8GB+ | 0.1002 | 86.3% | 87.5% | 161 | settings & logs |
| UD-Q2_K_XL | unsloth imatrix | 1.81 GB | 8GB+ | 0.2425 | 78.6% | 76.0% | 187 | settings & logs |
GB = GiB (1024³ bytes). Speeds from llama-bench on Ghost (RTX 5070 12GB, Ryzen 7 5800X, 64GB DDR4). On a different GPU, expect generation speed to scale roughly with memory bandwidth.
GPU too small? Rent one.
Run a 32B model at Q6/Q8, or a 70B, on a cloud GPU for an hour or two instead of buying hardware.
Per-second GPU pods. RTX 4090 / 5090 / A100 / H100 for big quants or BF16 reference runs.
Link coming soonMarketplace GPUs, often the cheapest way to try a 24-48GB card for an evening.
Link coming soon