Short answer: yes, but only just. Qwen3 14B at Q4_K_M needs about 11.4 GB at an 8K context, which is 95 percent of a 12 GB card. It runs entirely on the GPU if you keep the context short and close other GPU apps.
Check your own card with the Can My GPU Run This AI Model? calculator.
Memory needed
Qwen3 14B has 14.77 billion parameters, 40 layers and 8 KV heads of size 128. At Q4_K_M (about 4.9 bits per weight) the weights take about 9.0 GB. The KV cache adds 1.34 GB at 8K context and the runtime adds about 1 GB.
| Context | KV cache | Total (est.) | On 12 GB |
|---|---|---|---|
| 2K | 0.34 GB | 10.4 GB | Fits |
| 4K | 0.67 GB | 10.7 GB | Fits |
| 8K | 1.34 GB | 11.4 GB | Tight |
| 16K | 2.68 GB | 12.7 GB | No |
A Q8 KV cache (an option in Ollama and llama.cpp) roughly halves the cache cost and helps if you need more context.
Which 12 GB cards can run it?
All 12 GB cards have the same memory, so the difference is speed.
| GPU | Bandwidth | Speed (est.) |
|---|---|---|
| RTX 3060 12GB | 360 GB/s | 24 to 32 tok/s |
| RTX 4070 | 504 GB/s | 33 to 45 tok/s |
| RTX 4070 Super | 504 GB/s | 33 to 45 tok/s |
| RTX 5070 | 672 GB/s | 45 to 59 tok/s |
Speed is bandwidth divided by model size at 60 to 80 percent efficiency, so it is an estimate. We did not find reliable measured results for a 14B model on these cards.
If it does not fit
- Use a lower context length, or a Q8 KV cache.
- Try Gemma 3 12B (about 9.3 GB at 8K), which leaves more headroom.
- Use Q3-class quantization, which saves memory but lowers quality.
- Move to a 16 GB card, where Qwen3 14B fits easily.
Frequently asked questions
Can an RTX 3060 12GB run Qwen3 14B?
Yes, about 11.4 GB at 8K context, so keep the context short.
Is 12 GB enough for 14B models?
Just enough at Q4 with short context. A 16 GB card is more comfortable.
Can I run it at Q8 on 12 GB?
No. The Q8 weights alone are about 15.7 GB.
Sources and method
- Qwen3 14B architecture: Qwen3-14B config on Hugging Face.
- GPU bandwidth: see the individual GPU pages and our calculator data.
Estimates: weights = parameters × bits per weight; KV cache from the model’s published architecture; about 1 GB of runtime overhead. “Fits” means at most 92 percent of VRAM, “Tight” is 92 to 100 percent. Speed is memory bandwidth divided by model size at 60 to 80 percent efficiency. These are planning estimates, not our own benchmarks.
Last updated 3 Nov 2026.
