About this model
Qwen2.5 14B offers a clear quality step over 7B–8B models, particularly in reasoning and instruction following, at a memory cost that fits mainstream 12–16 GB GPUs. It is a common 'best quality my card allows' choice for 8 GB owners stepping up via offload.
What it is good for
- Higher-quality general assistant
- Reasoning-heavy drafting and analysis
- Multilingual document work
Limitations
- Needs 10–12 GB VRAM at 4-bit; 8 GB cards must offload some layers
- CPU offload drops speed substantially
Memory by quantization (estimates)
Weights at a given bit-width plus estimated runtime and context overhead. These are estimates — see the methodology.
| Quantization | Approx. memory | Fits a 10 GB card | Recommended class |
|---|---|---|---|
| Q4 (GGUF) | ~8.2 GB | Good | 10 GB+ |
| Q5 (GGUF) | ~9.6 GB | Good | 10 GB+ |
| Q8 (GGUF) | ~14.2 GB | Tight | 15 GB+ |
GPUs that can run it
FAQ
What VRAM does a 14B model need?
Roughly 8–9 GB at Q4 including overhead, so a 10–12 GB card runs it comfortably; 8 GB cards can only manage with partial CPU offload.
Is 14B much better than 8B?
For reasoning-heavy tasks and writing quality, noticeably. For simple chat, the difference is less obvious. It is a popular upgrade once VRAM allows.
Can it run on an RTX 4060 8 GB?
Only with CPU offload (llama.cpp style), which cuts speed significantly. On a 12 GB card like the 4070, it runs entirely in VRAM at good speed.
How does it compare to Phi-4 14B?
Both are 14B-class. Qwen2.5 14B is the more general all-rounder; Phi-4 leans into reasoning quality per parameter. Try both on your hardware.