Run MaziyarPanahi/Qwen3-8B-GGUF locally
MaziyarPanahi/Qwen3-8B-GGUF is a large language model with 8.19 billion parameters, built on the qwen3 architecture. It has been downloaded 276,412 times.
To run MaziyarPanahi/Qwen3-8B-GGUF locally at a 4,096-token context, its quantized versions need between 4.42 GB (Q2_K, lowest quality) and 16.63 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q3_K_L, needing about 5.49 GB. That means MaziyarPanahi/Qwen3-8B-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 |
|---|---|---|---|---|---|---|---|
| Q2_K | 3.21 | Low | 3.06 GB | 0.56 GB | 4.42 GB | 130.9 t/s | Fits in VRAM |
| Q3_K_M | 4.03 | Fair | 3.84 GB | 0.56 GB | 5.2 GB | 104.1 t/s | Fits in VRAM |
| Q3_K_L | 4.33 | Good | 4.13 GB | 0.56 GB | 5.49 GB | 96.9 t/s | Fits in VRAM |
| Q4_K_M | 4.91 | Good | 4.68 GB | 0.56 GB | 6.04 GB | 10.7 t/s | Offload |
| Q5_K_M | 5.71 | Very good | 5.45 GB | 0.56 GB | 6.81 GB | 9.2 t/s | Offload |
| Q6_K | 6.57 | Excellent | 6.26 GB | 0.56 GB | 7.63 GB | 8.0 t/s | Offload |
| GGUF | 16.01 | Excellent | 15.26 GB | 0.56 GB | 16.63 GB | 3.3 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 MaziyarPanahi/Qwen3-8B-GGUF?
You need about 5.49 GB of VRAM to run MaziyarPanahi/Qwen3-8B-GGUF entirely on the GPU using the Q3_K_L quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/Qwen3-8B-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run MaziyarPanahi/Qwen3-8B-GGUF fully on the GPU using Q6_K (about 7.63 GB).
Can I run MaziyarPanahi/Qwen3-8B-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Qwen3-8B-GGUF fully on the GPU using Q6_K (about 7.63 GB).
Can I run MaziyarPanahi/Qwen3-8B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Qwen3-8B-GGUF fully on the GPU using GGUF (about 16.63 GB).
What is the best quantization for MaziyarPanahi/Qwen3-8B-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.