Run unsloth/Qwen3.6-27B-MTP-GGUF locally
unsloth/Qwen3.6-27B-MTP-GGUF is a large language model with 27.32 billion parameters, built on the qwen35 architecture. It is released under the apache-2.0 license and has been downloaded 868,483 times.
To run unsloth/Qwen3.6-27B-MTP-GGUF locally at a 4,096-token context, its quantized versions need between 2.14 GB (F16, lowest quality) and 53.05 GB (BF16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is F32, needing about 2.99 GB. That means unsloth/Qwen3.6-27B-MTP-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 | 0.27 | Very low | 0.86 GB | 0.47 GB | 2.14 GB | 463.0 t/s | Fits in VRAM |
| F32 | 0.54 | Very low | 1.72 GB | 0.47 GB | 2.99 GB | 233.0 t/s | Fits in VRAM |
| IQ2_XXS | 2.8 | Low | 8.91 GB | 0.47 GB | 10.18 GB | 5.6 t/s | Offload |
| IQ2_M | 3.23 | Low | 10.27 GB | 0.47 GB | 11.55 GB | 4.9 t/s | Offload |
| Q2_K_XL | 3.53 | Fair | 11.21 GB | 0.47 GB | 12.49 GB | 4.5 t/s | Offload |
| IQ3_XXS | 3.57 | Fair | 11.37 GB | 0.47 GB | 12.64 GB | 4.4 t/s | Offload |
| Q3_K_S | 3.68 | Fair | 11.71 GB | 0.47 GB | 12.99 GB | 4.3 t/s | Offload |
| Q3_K_M | 4.05 | Fair | 12.87 GB | 0.47 GB | 14.14 GB | 3.9 t/s | Offload |
| Q3_K_XL | 4.33 | Good | 13.77 GB | 0.47 GB | 15.05 GB | 3.6 t/s | Offload |
| IQ4_XS | 4.6 | Good | 14.63 GB | 0.47 GB | 15.9 GB | 3.4 t/s | Offload |
| Q4_0 | 4.7 | Good | 14.95 GB | 0.47 GB | 16.23 GB | 3.3 t/s | Offload |
| Q4_K_S | 4.72 | Good | 15.01 GB | 0.47 GB | 16.29 GB | 3.3 t/s | Offload |
| IQ4_NL | 4.78 | Good | 15.22 GB | 0.47 GB | 16.49 GB | 3.3 t/s | Offload |
| Q4_K_M | 5.01 | Very good | 15.93 GB | 0.47 GB | 17.21 GB | 3.1 t/s | Offload |
| Q4_1 | 5.14 | Very good | 16.34 GB | 0.47 GB | 17.61 GB | 3.1 t/s | Offload |
| Q4_K_XL | 5.24 | Very good | 16.68 GB | 0.47 GB | 17.95 GB | 3.0 t/s | Offload |
| Q5_K_S | 5.64 | Very good | 17.95 GB | 0.47 GB | 19.22 GB | 2.8 t/s | Offload |
| Q5_K_M | 5.81 | Very good | 18.47 GB | 0.47 GB | 19.75 GB | 2.7 t/s | Offload |
| Q5_K_XL | 5.96 | Very good | 18.95 GB | 0.47 GB | 20.23 GB | 2.6 t/s | Offload |
| Q6_K | 6.7 | Excellent | 21.31 GB | 0.47 GB | 22.59 GB | 2.3 t/s | Offload |
| Q6_K_XL | 7.62 | Excellent | 24.23 GB | 0.47 GB | 25.5 GB | — | Insufficient |
| Q8_0 | 8.51 | Excellent | 27.05 GB | 0.47 GB | 28.33 GB | — | Insufficient |
| Q8_K_XL | 10.48 | Excellent | 33.32 GB | 0.47 GB | 34.59 GB | — | Insufficient |
| BF16 | 16.28 | Excellent | 51.77 GB | 0.47 GB | 53.05 GB | — | Insufficient |
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/Qwen3.6-27B-MTP-GGUF?
You need about 2.99 GB of VRAM to run unsloth/Qwen3.6-27B-MTP-GGUF entirely on the GPU using the F32 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run unsloth/Qwen3.6-27B-MTP-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run unsloth/Qwen3.6-27B-MTP-GGUF fully on the GPU using F32 (about 2.99 GB).
Can I run unsloth/Qwen3.6-27B-MTP-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run unsloth/Qwen3.6-27B-MTP-GGUF fully on the GPU using IQ4_XS (about 15.9 GB).
Can I run unsloth/Qwen3.6-27B-MTP-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run unsloth/Qwen3.6-27B-MTP-GGUF fully on the GPU using Q6_K (about 22.59 GB).
What is the best quantization for unsloth/Qwen3.6-27B-MTP-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.