Qwen2.5-Coder-14B-Instruct GGUF size and VRAM requirements
Qwen/Qwen2.5-Coder-14B-Instruct-GGUF is a large code-focused language model with 14.77 billion parameters, built on the qwen2 architecture. It is released under the apache-2.0 license and has been downloaded 79,461 times.
To run Qwen/Qwen2.5-Coder-14B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 6.92 GB (Q2_K, lowest quality) and 56.59 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q2_K, needing about 6.92 GB. That means Qwen/Qwen2.5-Coder-14B-Instruct-GGUF fits entirely in the VRAM of an 8 GB GPU or larger, running fully on the GPU.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q2_K | 3.13 | Low | 5.37 GB | 0.75 GB | 6.92 GB | 74.4 t/s | Fits in VRAM |
| Q3_K_M | 3.98 | Fair | 6.84 GB | 0.75 GB | 8.39 GB | 7.3 t/s | Offload |
| Q4_0 | 4.61 | Good | 7.93 GB | 0.75 GB | 9.48 GB | 6.3 t/s | Offload |
| Q4_K_M | 9.74 | Excellent | 16.74 GB | 0.75 GB | 18.29 GB | 3.0 t/s | Offload |
| Q5_0 | 11.12 | Excellent | 19.12 GB | 0.75 GB | 20.67 GB | 2.6 t/s | Offload |
| Q5_K_M | 11.38 | Excellent | 19.57 GB | 0.75 GB | 21.12 GB | 2.6 t/s | Offload |
| Q6_K | 13.13 | Excellent | 22.58 GB | 0.75 GB | 24.13 GB | — | Insufficient |
| Q8_0 | 17.01 | Excellent | 29.25 GB | 0.75 GB | 30.8 GB | — | Insufficient |
| GGUF | 32.01 | Excellent | 55.04 GB | 0.75 GB | 56.59 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-14B-Instruct-GGUF?
You need about 6.92 GB of VRAM to run Qwen/Qwen2.5-Coder-14B-Instruct-GGUF entirely on the GPU using the Q2_K quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run Qwen/Qwen2.5-Coder-14B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run Qwen/Qwen2.5-Coder-14B-Instruct-GGUF fully on the GPU using Q2_K (about 6.92 GB).
Can I run Qwen/Qwen2.5-Coder-14B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run Qwen/Qwen2.5-Coder-14B-Instruct-GGUF fully on the GPU using Q4_0 (about 9.48 GB).
Can I run Qwen/Qwen2.5-Coder-14B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run Qwen/Qwen2.5-Coder-14B-Instruct-GGUF fully on the GPU using Q5_K_M (about 21.12 GB).
What is the best quantization for Qwen/Qwen2.5-Coder-14B-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.