Run Qwen/Qwen2.5-0.5B-Instruct-GGUF locally
Qwen/Qwen2.5-0.5B-Instruct-GGUF is a compact instruction-tuned chat model with 0.63 billion parameters, built on the qwen2 architecture. It is released under the apache-2.0 license and has been downloaded 199,385 times.
To run Qwen/Qwen2.5-0.5B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 1.23 GB (Q2_K, lowest quality) and 2.03 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 2.03 GB. That means Qwen/Qwen2.5-0.5B-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 | 5.27 | Very good | 0.39 GB | 0.05 GB | 1.23 GB | 1034.5 t/s | Fits in VRAM |
| Q4_0 | 5.44 | Very good | 0.4 GB | 0.05 GB | 1.25 GB | 1001.8 t/s | Fits in VRAM |
| Q3_K_M | 5.48 | Very good | 0.4 GB | 0.05 GB | 1.25 GB | 994.1 t/s | Fits in VRAM |
| Q5_0 | 6.23 | Very good | 0.46 GB | 0.05 GB | 1.3 GB | 875.7 t/s | Fits in VRAM |
| Q4_K_M | 6.24 | Very good | 0.46 GB | 0.05 GB | 1.3 GB | 874.0 t/s | Fits in VRAM |
| Q5_K_M | 6.63 | Excellent | 0.49 GB | 0.05 GB | 1.33 GB | 822.5 t/s | Fits in VRAM |
| Q6_K | 8.26 | Excellent | 0.61 GB | 0.05 GB | 1.45 GB | 660.4 t/s | Fits in VRAM |
| Q8_0 | 8.58 | Excellent | 0.63 GB | 0.05 GB | 1.48 GB | 635.6 t/s | Fits in VRAM |
| GGUF | 16.08 | Excellent | 1.18 GB | 0.05 GB | 2.03 GB | 339.1 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 Qwen/Qwen2.5-0.5B-Instruct-GGUF?
You need about 2.03 GB of VRAM to run Qwen/Qwen2.5-0.5B-Instruct-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 Qwen/Qwen2.5-0.5B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run Qwen/Qwen2.5-0.5B-Instruct-GGUF fully on the GPU using GGUF (about 2.03 GB).
Can I run Qwen/Qwen2.5-0.5B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run Qwen/Qwen2.5-0.5B-Instruct-GGUF fully on the GPU using GGUF (about 2.03 GB).
Can I run Qwen/Qwen2.5-0.5B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run Qwen/Qwen2.5-0.5B-Instruct-GGUF fully on the GPU using GGUF (about 2.03 GB).
What is the best quantization for Qwen/Qwen2.5-0.5B-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.