Run MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF locally
MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF is a mid-size instruction-tuned chat model with 4.02 billion parameters, built on the qwen3 architecture. It has been downloaded 194,010 times.
To run MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF locally at a 4,096-token context, its quantized versions need between 2.71 GB (Q2_K, lowest quality) and 8.65 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q6_K, needing about 4.23 GB. That means MaziyarPanahi/Qwen3-4B-Instruct-2507-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 | 3.32 | Fair | 1.55 GB | 0.35 GB | 2.71 GB | 257.3 t/s | Fits in VRAM |
| Q3_K_M | 4.13 | Fair | 1.93 GB | 0.35 GB | 3.08 GB | 206.9 t/s | Fits in VRAM |
| Q3_K_L | 4.45 | Good | 2.09 GB | 0.35 GB | 3.24 GB | 191.8 t/s | Fits in VRAM |
| Q4_K_M | 4.97 | Good | 2.33 GB | 0.35 GB | 3.48 GB | 172.0 t/s | Fits in VRAM |
| Q5_K_M | 5.75 | Very good | 2.69 GB | 0.35 GB | 3.84 GB | 148.6 t/s | Fits in VRAM |
| Q6_K | 6.58 | Excellent | 3.08 GB | 0.35 GB | 4.23 GB | 129.9 t/s | Fits in VRAM |
| GGUF | 16.01 | Excellent | 7.5 GB | 0.35 GB | 8.65 GB | 6.7 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/Qwen3-4B-Instruct-2507-GGUF?
You need about 4.23 GB of VRAM to run MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF entirely on the GPU using the Q6_K quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF fully on the GPU using Q6_K (about 4.23 GB).
Can I run MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF fully on the GPU using GGUF (about 8.65 GB).
Can I run MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF fully on the GPU using GGUF (about 8.65 GB).
What is the best quantization for MaziyarPanahi/Qwen3-4B-Instruct-2507-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.