Run bartowski/Qwen2.5-14B-Instruct-GGUF locally
Qwen2.5-14B-Instruct is a 14.77 billion parameter AI model licensed under Apache-2.0, designed for instruction-based tasks. It belongs to the Qwen family, optimized for general-purpose language understanding and generation. The model is intended for deployment in applications requiring structured responses to user prompts.
To run bartowski/Qwen2.5-14B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 6.54 GB (IQ2_M, lowest quality) and 29.07 GB (F16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is IQ3_M, needing about 7.99 GB. That means bartowski/Qwen2.5-14B-Instruct-GGUF fits entirely in the VRAM of an 8 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ2_M | 2.9 | Low | 4.99 GB | 0.75 GB | 6.54 GB | 80.2 t/s | Fits in VRAM |
| Q2_K | 3.13 | Low | 5.37 GB | 0.75 GB | 6.92 GB | 74.4 t/s | Fits in VRAM |
| IQ3_XS | 3.46 | Fair | 5.94 GB | 0.75 GB | 7.49 GB | 67.3 t/s | Fits in VRAM |
| Q2_K_L | 3.54 | Fair | 6.08 GB | 0.75 GB | 7.63 GB | 65.8 t/s | Fits in VRAM |
| Q3_K_S | 3.61 | Fair | 6.2 GB | 0.75 GB | 7.75 GB | 64.5 t/s | Fits in VRAM |
| IQ3_M | 3.75 | Fair | 6.44 GB | 0.75 GB | 7.99 GB | 62.1 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 |
| Q3_K_L | 4.29 | Good | 7.38 GB | 0.75 GB | 8.93 GB | 6.8 t/s | Offload |
| IQ4_XS | 4.4 | Good | 7.56 GB | 0.75 GB | 9.11 GB | 6.6 t/s | Offload |
| Q4_0_4_4 | 4.61 | Good | 7.93 GB | 0.75 GB | 9.48 GB | 6.3 t/s | Offload |
| Q4_0_4_8 | 4.61 | Good | 7.93 GB | 0.75 GB | 9.48 GB | 6.3 t/s | Offload |
| Q4_0_8_8 | 4.61 | Good | 7.93 GB | 0.75 GB | 9.48 GB | 6.3 t/s | Offload |
| Q4_0 | 4.63 | Good | 7.96 GB | 0.75 GB | 9.51 GB | 6.3 t/s | Offload |
| Q4_K_S | 4.64 | Good | 7.98 GB | 0.75 GB | 9.53 GB | 6.3 t/s | Offload |
| Q3_K_XL | 4.66 | Good | 8.01 GB | 0.75 GB | 9.56 GB | 6.2 t/s | Offload |
| Q4_K_M | 4.87 | Good | 8.37 GB | 0.75 GB | 9.92 GB | 6.0 t/s | Offload |
| Q4_K_L | 5.18 | Very good | 8.91 GB | 0.75 GB | 10.46 GB | 5.6 t/s | Offload |
| Q5_K_S | 5.56 | Very good | 9.56 GB | 0.75 GB | 11.11 GB | 5.2 t/s | Offload |
| Q5_K_M | 5.69 | Very good | 9.79 GB | 0.75 GB | 11.34 GB | 5.1 t/s | Offload |
| Q5_K_L | 5.95 | Very good | 10.23 GB | 0.75 GB | 11.78 GB | 4.9 t/s | Offload |
| Q6_K | 6.57 | Excellent | 11.29 GB | 0.75 GB | 12.84 GB | 4.4 t/s | Offload |
| Q6_K_L | 6.77 | Excellent | 11.64 GB | 0.75 GB | 13.19 GB | 4.3 t/s | Offload |
| Q8_0 | 8.5 | Excellent | 14.62 GB | 0.75 GB | 16.17 GB | 3.4 t/s | Offload |
| F16 | 16.0 | Excellent | 27.52 GB | 0.75 GB | 29.07 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 bartowski/Qwen2.5-14B-Instruct-GGUF?
You need about 7.99 GB of VRAM to run bartowski/Qwen2.5-14B-Instruct-GGUF entirely on the GPU using the IQ3_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run bartowski/Qwen2.5-14B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run bartowski/Qwen2.5-14B-Instruct-GGUF fully on the GPU using IQ3_M (about 7.99 GB).
Can I run bartowski/Qwen2.5-14B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run bartowski/Qwen2.5-14B-Instruct-GGUF fully on the GPU using Q6_K_L (about 13.19 GB).
Can I run bartowski/Qwen2.5-14B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run bartowski/Qwen2.5-14B-Instruct-GGUF fully on the GPU using Q8_0 (about 16.17 GB).
What is the best quantization for bartowski/Qwen2.5-14B-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.