Run unsloth/Qwen3.5-9B-GGUF locally
unsloth/Qwen3.5-9B-GGUF is a large language model with 8.95 billion parameters, built on the qwen35 architecture. It is released under the apache-2.0 license and has been downloaded 1,047,718 times.
To run unsloth/Qwen3.5-9B-GGUF locally at a 4,096-token context, its quantized versions need between 2.13 GB (F16, lowest quality) and 18.82 GB (BF16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q5_K_XL, needing about 7.56 GB. That means unsloth/Qwen3.5-9B-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| F16 | 0.82 | Very low | 0.86 GB | 0.47 GB | 2.13 GB | 467.8 t/s | Fits in VRAM |
| F32 | 1.63 | Very low | 1.7 GB | 0.47 GB | 2.97 GB | 235.5 t/s | Fits in VRAM |
| IQ2_XXS | 2.85 | Low | 2.97 GB | 0.47 GB | 4.25 GB | 134.6 t/s | Fits in VRAM |
| IQ2_M | 3.26 | Low | 3.4 GB | 0.47 GB | 4.67 GB | 117.7 t/s | Fits in VRAM |
| IQ3_XXS | 3.59 | Fair | 3.74 GB | 0.47 GB | 5.02 GB | 106.9 t/s | Fits in VRAM |
| Q2_K_XL | 3.68 | Fair | 3.84 GB | 0.47 GB | 5.11 GB | 104.2 t/s | Fits in VRAM |
| Q3_K_S | 3.86 | Fair | 4.02 GB | 0.47 GB | 5.3 GB | 99.5 t/s | Fits in VRAM |
| Q3_K_M | 4.18 | Fair | 4.35 GB | 0.47 GB | 5.63 GB | 91.9 t/s | Fits in VRAM |
| Q3_K_XL | 4.52 | Good | 4.71 GB | 0.47 GB | 5.98 GB | 85.0 t/s | Fits in VRAM |
| IQ4_XS | 4.62 | Good | 4.81 GB | 0.47 GB | 6.09 GB | 83.1 t/s | Fits in VRAM |
| IQ4_NL | 4.8 | Good | 5.0 GB | 0.47 GB | 6.28 GB | 80.0 t/s | Fits in VRAM |
| Q4_0 | 4.81 | Good | 5.01 GB | 0.47 GB | 6.28 GB | 79.8 t/s | Fits in VRAM |
| Q4_K_S | 4.82 | Good | 5.02 GB | 0.47 GB | 6.3 GB | 79.6 t/s | Fits in VRAM |
| Q4_K_M | 5.08 | Very good | 5.29 GB | 0.47 GB | 6.57 GB | 75.6 t/s | Fits in VRAM |
| Q4_1 | 5.22 | Very good | 5.44 GB | 0.47 GB | 6.71 GB | 73.6 t/s | Fits in VRAM |
| Q4_K_XL | 5.33 | Very good | 5.56 GB | 0.47 GB | 6.83 GB | 72.0 t/s | Fits in VRAM |
| Q5_K_S | 5.68 | Very good | 5.92 GB | 0.47 GB | 7.2 GB | 67.5 t/s | Fits in VRAM |
| Q5_K_M | 5.88 | Very good | 6.13 GB | 0.47 GB | 7.4 GB | 65.3 t/s | Fits in VRAM |
| Q5_K_XL | 6.03 | Very good | 6.28 GB | 0.47 GB | 7.56 GB | 63.7 t/s | Fits in VRAM |
| Q6_K | 6.66 | Excellent | 6.95 GB | 0.47 GB | 8.22 GB | 7.2 t/s | Offload |
| Q6_K_XL | 7.82 | Excellent | 8.16 GB | 0.47 GB | 9.43 GB | 6.1 t/s | Offload |
| Q8_0 | 8.51 | Excellent | 8.87 GB | 0.47 GB | 10.15 GB | 5.6 t/s | Offload |
| Q8_K_XL | 11.59 | Excellent | 12.08 GB | 0.47 GB | 13.36 GB | 4.1 t/s | Offload |
| BF16 | 16.84 | Excellent | 17.55 GB | 0.47 GB | 18.82 GB | 2.8 t/s | Offload |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run unsloth/Qwen3.5-9B-GGUF?
You need about 5.98 GB of VRAM to run unsloth/Qwen3.5-9B-GGUF entirely on the GPU using the Q3_K_XL quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run unsloth/Qwen3.5-9B-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run unsloth/Qwen3.5-9B-GGUF fully on the GPU using Q5_K_XL (about 7.56 GB).
Can I run unsloth/Qwen3.5-9B-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run unsloth/Qwen3.5-9B-GGUF fully on the GPU using Q8_K_XL (about 13.36 GB).
Can I run unsloth/Qwen3.5-9B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run unsloth/Qwen3.5-9B-GGUF fully on the GPU using BF16 (about 18.82 GB).
What is the best quantization for unsloth/Qwen3.5-9B-GGUF?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.