Run Qwen/Qwen2.5-3B-Instruct-GGUF locally
Qwen/Qwen2.5-3B-Instruct-GGUF is a mid-size instruction-tuned chat model with 3.4 billion parameters, built on the qwen2 architecture. It is released under the other license and has been downloaded 228,306 times.
To run Qwen/Qwen2.5-3B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 2.22 GB (Q2_K, lowest quality) and 7.27 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 3.54 GB. That means Qwen/Qwen2.5-3B-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 |
|---|---|---|---|---|---|---|---|
| Q2_K | 3.24 | Low | 1.28 GB | 0.14 GB | 2.22 GB | 311.9 t/s | Fits in VRAM |
| Q3_K_M | 4.06 | Fair | 1.61 GB | 0.14 GB | 2.55 GB | 249.1 t/s | Fits in VRAM |
| Q4_0 | 4.7 | Good | 1.86 GB | 0.14 GB | 2.8 GB | 215.0 t/s | Fits in VRAM |
| Q4_K_M | 4.96 | Good | 1.96 GB | 0.14 GB | 2.9 GB | 204.0 t/s | Fits in VRAM |
| Q5_0 | 5.61 | Very good | 2.22 GB | 0.14 GB | 3.16 GB | 180.2 t/s | Fits in VRAM |
| Q5_K_M | 5.74 | Very good | 2.27 GB | 0.14 GB | 3.21 GB | 176.1 t/s | Fits in VRAM |
| Q6_K | 6.58 | Excellent | 2.6 GB | 0.14 GB | 3.54 GB | 153.8 t/s | Fits in VRAM |
| Q8_0 | 8.52 | Excellent | 3.37 GB | 0.14 GB | 4.31 GB | 14.8 t/s | Offload |
| GGUF | 16.02 | Excellent | 6.33 GB | 0.14 GB | 7.27 GB | 7.9 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 Qwen/Qwen2.5-3B-Instruct-GGUF?
You need about 4.31 GB of VRAM to run Qwen/Qwen2.5-3B-Instruct-GGUF entirely on the GPU using the Q8_0 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run Qwen/Qwen2.5-3B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run Qwen/Qwen2.5-3B-Instruct-GGUF fully on the GPU using GGUF (about 7.27 GB).
Can I run Qwen/Qwen2.5-3B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run Qwen/Qwen2.5-3B-Instruct-GGUF fully on the GPU using GGUF (about 7.27 GB).
Can I run Qwen/Qwen2.5-3B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run Qwen/Qwen2.5-3B-Instruct-GGUF fully on the GPU using GGUF (about 7.27 GB).
What is the best quantization for Qwen/Qwen2.5-3B-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.