Qwen 3 14B
14B Qwen 3. Fits on 12GB cards at Q4. Strong default for users with a single mid-range GPU.
The most capability per VRAM available. Qwen 3 14B at Q4_K_M fits in ~9 GB, leaving room for full 32K context on a 16 GB card, and in thinking mode it punches well above its parameter weight on math and code.
Strengths- 9 GB at Q4_K_M — leaves headroom on RTX 3060 12 GB and RTX 4060 Ti 16 GB.
- Hybrid reasoning lifts hard-task scores by 10–15 points GSM8K-equivalent vs non-thinking.
- Long context recall holds up out to 32K in practice — better than Qwen 2.5 14B.
- Thinking-mode latency is real — budget 2–3× tokens for hard prompts.
- Tool use still rougher than Llama — function-call loops occasionally derail.
- License caps unchanged from Qwen 2.5.
- Q4_K_M (9.1 GB): 60–75 tok/s decode (non-thinking); same speed thinking but 2–3× output
- Q5_K_M (10.5 GB): 50–62 tok/s
- Q8_0 (15.8 GB): 36–46 tok/s
Yes, for RTX 3060 12 GB / 4060 Ti 16 GB / 4070 / 5070 owners who want the best capability for their hardware tier. New default for 12–16 GB cards. No, for users on 24 GB cards — jump to Qwen 3 32B or QwQ 32B; 14B is the wrong tier for that VRAM.
How it compares- vs Qwen 2.5 14B → Qwen 3 14B with thinking mode is materially better; non-thinking is roughly even. Pick Qwen 3 going forward.
- vs Phi-4 14B → close call. Phi-4 has more polished reasoning; Qwen 3 14B has hybrid mode. Pick Phi-4 for steady reasoning, Qwen 3 14B for flexibility.
- vs Mistral Small 3 24B → Mistral Small is bigger, slightly stronger absolute capability; Qwen 3 14B is much more memory-efficient.
- vs Qwen 3 8B → 14B is meaningfully smarter; pick 14B if VRAM allows.
ollama pull qwen3:14b
ollama run qwen3:14b
›Why this rating
8.8/10 — the new 14B-class king. Qwen 3 14B in thinking mode hits performance bands previously reserved for 30B-class models, while staying inside 12 GB VRAM at Q4. The model 16 GB GPU owners should default to.
Overview
14B Qwen 3. Fits on 12GB cards at Q4. Strong default for users with a single mid-range GPU.
Strengths
- Fits on RTX 3060/4060 Ti
- Apache 2.0
Weaknesses
- Some Chinese tokenizer quirks
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 8.4 GB | 11 GB |
| Q8_0 | 15.0 GB | 18 GB |
Get the model
Ollama
One-line install
ollama run qwen3:14bRead our Ollama review →HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Qwen 3 14B.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Qwen 3 14B?
Can I use Qwen 3 14B commercially?
What's the context length of Qwen 3 14B?
How do I install Qwen 3 14B with Ollama?
Source: huggingface.co/Qwen/Qwen3-14B
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.