Run Qwen/Qwen2.5-1.5B-Instruct-GGUF locally
Qwen/Qwen2.5-1.5B-Instruct-GGUF is a compact instruction-tuned chat model with 1.78 billion parameters, built on the qwen2 architecture. It is released under the apache-2.0 license and has been downloaded 253,271 times.
To run Qwen/Qwen2.5-1.5B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 1.61 GB (Q2_K, lowest quality) and 4.23 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 2.67 GB. That means Qwen/Qwen2.5-1.5B-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 | 3.39 | Fair | 0.7 GB | 0.11 GB | 1.61 GB | 570.5 t/s | Fits in VRAM |
| Q3_K_M | 4.16 | Fair | 0.86 GB | 0.11 GB | 1.77 GB | 464.6 t/s | Fits in VRAM |
| Q4_0 | 4.8 | Good | 0.99 GB | 0.11 GB | 1.9 GB | 402.8 t/s | Fits in VRAM |
| Q4_K_M | 5.03 | Very good | 1.04 GB | 0.11 GB | 1.95 GB | 384.4 t/s | Fits in VRAM |
| Q5_0 | 5.67 | Very good | 1.17 GB | 0.11 GB | 2.08 GB | 341.1 t/s | Fits in VRAM |
| Q5_K_M | 5.79 | Very good | 1.2 GB | 0.11 GB | 2.11 GB | 334.1 t/s | Fits in VRAM |
| Q6_K | 6.59 | Excellent | 1.36 GB | 0.11 GB | 2.27 GB | 293.3 t/s | Fits in VRAM |
| Q8_0 | 8.53 | Excellent | 1.76 GB | 0.11 GB | 2.67 GB | 226.7 t/s | Fits in VRAM |
| GGUF | 16.03 | Excellent | 3.32 GB | 0.11 GB | 4.23 GB | 15.1 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/Qwen2.5-1.5B-Instruct-GGUF?
You need about 4.23 GB of VRAM to run Qwen/Qwen2.5-1.5B-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/Qwen2.5-1.5B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run Qwen/Qwen2.5-1.5B-Instruct-GGUF fully on the GPU using GGUF (about 4.23 GB).
Can I run Qwen/Qwen2.5-1.5B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run Qwen/Qwen2.5-1.5B-Instruct-GGUF fully on the GPU using GGUF (about 4.23 GB).
Can I run Qwen/Qwen2.5-1.5B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run Qwen/Qwen2.5-1.5B-Instruct-GGUF fully on the GPU using GGUF (about 4.23 GB).
What is the best quantization for Qwen/Qwen2.5-1.5B-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.