Run MaziyarPanahi/Qwen3-1.7B-GGUF locally
MaziyarPanahi/Qwen3-1.7B-GGUF is a mid-size language model with 2.03 billion parameters, built on the qwen3 architecture. It has been downloaded 273,448 times.
To run MaziyarPanahi/Qwen3-1.7B-GGUF locally at a 4,096-token context, its quantized versions need between 2.06 GB (Q2_K, lowest quality) and 5.03 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 5.03 GB. That means MaziyarPanahi/Qwen3-1.7B-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 | 3.46 | Fair | 0.82 GB | 0.44 GB | 2.06 GB | 488.1 t/s | Fits in VRAM |
| Q3_K_M | 4.23 | Good | 1.0 GB | 0.44 GB | 2.24 GB | 400.2 t/s | Fits in VRAM |
| Q3_K_L | 4.48 | Good | 1.06 GB | 0.44 GB | 2.3 GB | 377.7 t/s | Fits in VRAM |
| Q4_K_M | 5.05 | Very good | 1.19 GB | 0.44 GB | 2.43 GB | 334.9 t/s | Fits in VRAM |
| Q5_K_M | 5.8 | Very good | 1.37 GB | 0.44 GB | 2.61 GB | 291.8 t/s | Fits in VRAM |
| Q6_K | 6.59 | Excellent | 1.56 GB | 0.44 GB | 2.8 GB | 256.7 t/s | Fits in VRAM |
| GGUF | 16.02 | Excellent | 3.79 GB | 0.44 GB | 5.03 GB | 105.5 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/Qwen3-1.7B-GGUF?
You need about 5.03 GB of VRAM to run MaziyarPanahi/Qwen3-1.7B-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/Qwen3-1.7B-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run MaziyarPanahi/Qwen3-1.7B-GGUF fully on the GPU using GGUF (about 5.03 GB).
Can I run MaziyarPanahi/Qwen3-1.7B-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Qwen3-1.7B-GGUF fully on the GPU using GGUF (about 5.03 GB).
Can I run MaziyarPanahi/Qwen3-1.7B-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Qwen3-1.7B-GGUF fully on the GPU using GGUF (about 5.03 GB).
What is the best quantization for MaziyarPanahi/Qwen3-1.7B-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.