Run Qwen/Qwen2.5-Coder-7B-Instruct-GGUF locally
Qwen/Qwen2.5-Coder-7B-Instruct-GGUF is a mid-size code-focused language model with 7.62 billion parameters, built on the qwen2 architecture. It is released under the apache-2.0 license and has been downloaded 180,170 times.
To run Qwen/Qwen2.5-Coder-7B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 3.83 GB (Q2_K, lowest quality) and 29.4 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q5_0, needing about 5.97 GB. That means Qwen/Qwen2.5-Coder-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 |
|---|---|---|---|---|---|---|---|
| Q2_K | 3.17 | Low | 2.81 GB | 0.22 GB | 3.83 GB | 142.4 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 |
| Q5_0 | 5.58 | Very good | 4.95 GB | 0.22 GB | 5.97 GB | 80.8 t/s | Fits in VRAM |
| Q4_0 | 9.31 | Excellent | 8.25 GB | 0.22 GB | 9.27 GB | 6.1 t/s | Offload |
| Q4_K_M | 9.84 | Excellent | 8.72 GB | 0.22 GB | 9.74 GB | 5.7 t/s | Offload |
| Q5_K_M | 11.44 | Excellent | 10.14 GB | 0.22 GB | 11.16 GB | 4.9 t/s | Offload |
| Q6_K | 13.14 | Excellent | 11.65 GB | 0.22 GB | 12.67 GB | 4.3 t/s | Offload |
| Q8_0 | 17.01 | Excellent | 15.08 GB | 0.22 GB | 16.1 GB | 3.3 t/s | Offload |
| GGUF | 32.01 | Excellent | 28.38 GB | 0.22 GB | 29.4 GB | — | Insufficient |
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-Coder-7B-Instruct-GGUF?
You need about 5.97 GB of VRAM to run Qwen/Qwen2.5-Coder-7B-Instruct-GGUF entirely on the GPU using the Q5_0 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run Qwen/Qwen2.5-Coder-7B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run Qwen/Qwen2.5-Coder-7B-Instruct-GGUF fully on the GPU using Q5_0 (about 5.97 GB).
Can I run Qwen/Qwen2.5-Coder-7B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run Qwen/Qwen2.5-Coder-7B-Instruct-GGUF fully on the GPU using Q6_K (about 12.67 GB).
Can I run Qwen/Qwen2.5-Coder-7B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run Qwen/Qwen2.5-Coder-7B-Instruct-GGUF fully on the GPU using Q8_0 (about 16.1 GB).
What is the best quantization for Qwen/Qwen2.5-Coder-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.