Run MaziyarPanahi/Qwen2.5-7B-Instruct-GGUF locally
MaziyarPanahi/Qwen2.5-7B-Instruct-GGUF is a mid-size instruction-tuned chat model with 7.62 billion parameters, built on the qwen2 architecture. It has been downloaded 164,468 times.
To run MaziyarPanahi/Qwen2.5-7B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 2.79 GB (IQ1_S, lowest quality) and 15.21 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q8_0, needing about 8.56 GB. That means MaziyarPanahi/Qwen2.5-7B-Instruct-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 |
|---|---|---|---|---|---|---|---|
| IQ1_S | 2.0 | Very low | 1.77 GB | 0.22 GB | 2.79 GB | 225.6 t/s | Fits in VRAM |
| IQ1_M | 2.15 | Very low | 1.9 GB | 0.22 GB | 2.92 GB | 210.3 t/s | Fits in VRAM |
| IQ2_XS | 2.59 | Very low | 2.3 GB | 0.22 GB | 3.32 GB | 174.0 t/s | Fits in VRAM |
| Q2_K | 3.17 | Low | 2.81 GB | 0.22 GB | 3.83 GB | 142.4 t/s | Fits in VRAM |
| IQ3_XS | 3.52 | Fair | 3.12 GB | 0.22 GB | 4.14 GB | 128.4 t/s | Fits in VRAM |
| Q3_K_S | 3.67 | Fair | 3.25 GB | 0.22 GB | 4.27 GB | 123.0 t/s | Fits in VRAM |
| Q3_K_M | 4.0 | Fair | 3.55 GB | 0.22 GB | 4.57 GB | 112.8 t/s | Fits in VRAM |
| Q3_K_L | 4.29 | Good | 3.81 GB | 0.22 GB | 4.83 GB | 105.1 t/s | Fits in VRAM |
| IQ4_XS | 4.43 | Good | 3.93 GB | 0.22 GB | 4.95 GB | 101.8 t/s | Fits in VRAM |
| Q4_K_S | 4.68 | Good | 4.15 GB | 0.22 GB | 5.17 GB | 96.3 t/s | Fits in VRAM |
| Q4_K_M | 4.92 | Good | 4.36 GB | 0.22 GB | 5.38 GB | 91.7 t/s | Fits in VRAM |
| Q5_K_S | 5.58 | Very good | 4.95 GB | 0.22 GB | 5.97 GB | 80.8 t/s | Fits in VRAM |
| Q5_K_M | 5.72 | Very good | 5.07 GB | 0.22 GB | 6.09 GB | 78.9 t/s | Fits in VRAM |
| Q6_K | 6.57 | Excellent | 5.82 GB | 0.22 GB | 6.84 GB | 68.7 t/s | Fits in VRAM |
| Q8_0 | 8.51 | Excellent | 7.54 GB | 0.22 GB | 8.56 GB | 53.0 t/s | Fits in VRAM |
| GGUF | 16.01 | Excellent | 14.19 GB | 0.22 GB | 15.21 GB | 3.5 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/Qwen2.5-7B-Instruct-GGUF?
You need about 5.97 GB of VRAM to run MaziyarPanahi/Qwen2.5-7B-Instruct-GGUF entirely on the GPU using the Q5_K_S quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/Qwen2.5-7B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run MaziyarPanahi/Qwen2.5-7B-Instruct-GGUF fully on the GPU using Q6_K (about 6.84 GB).
Can I run MaziyarPanahi/Qwen2.5-7B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Qwen2.5-7B-Instruct-GGUF fully on the GPU using GGUF (about 15.21 GB).
Can I run MaziyarPanahi/Qwen2.5-7B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Qwen2.5-7B-Instruct-GGUF fully on the GPU using GGUF (about 15.21 GB).
What is the best quantization for MaziyarPanahi/Qwen2.5-7B-Instruct-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.