Run MaziyarPanahi/Qwen3-0.6B-GGUF locally
MaziyarPanahi/Qwen3-0.6B-GGUF is a compact language model with 0.75 billion parameters, built on the qwen3 architecture. It has been downloaded 280,888 times.
To run MaziyarPanahi/Qwen3-0.6B-GGUF locally at a 4,096-token context, its quantized versions need between 1.34 GB (Q2_K, lowest quality) and 2.42 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is GGUF, needing about 2.42 GB. That means MaziyarPanahi/Qwen3-0.6B-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 | 3.7 | Fair | 0.32 GB | 0.22 GB | 1.34 GB | 1236.7 t/s | Fits in VRAM |
| Q3_K_M | 4.41 | Good | 0.39 GB | 0.22 GB | 1.4 GB | 1037.5 t/s | Fits in VRAM |
| Q3_K_L | 4.63 | Good | 0.41 GB | 0.22 GB | 1.42 GB | 986.6 t/s | Fits in VRAM |
| Q4_K_M | 5.15 | Very good | 0.45 GB | 0.22 GB | 1.47 GB | 887.0 t/s | Fits in VRAM |
| Q5_K_M | 5.87 | Very good | 0.51 GB | 0.22 GB | 1.53 GB | 779.0 t/s | Fits in VRAM |
| Q6_K | 6.63 | Excellent | 0.58 GB | 0.22 GB | 1.6 GB | 689.7 t/s | Fits in VRAM |
| GGUF | 16.06 | Excellent | 1.41 GB | 0.22 GB | 2.42 GB | 284.6 t/s | Fits in VRAM |
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/Qwen3-0.6B-GGUF?
You need about 2.42 GB of VRAM to run MaziyarPanahi/Qwen3-0.6B-GGUF entirely on the GPU using the GGUF quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/Qwen3-0.6B-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run MaziyarPanahi/Qwen3-0.6B-GGUF fully on the GPU using GGUF (about 2.42 GB).
Can I run MaziyarPanahi/Qwen3-0.6B-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Qwen3-0.6B-GGUF fully on the GPU using GGUF (about 2.42 GB).
Can I run MaziyarPanahi/Qwen3-0.6B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Qwen3-0.6B-GGUF fully on the GPU using GGUF (about 2.42 GB).
What is the best quantization for MaziyarPanahi/Qwen3-0.6B-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.