# Qwen3 14B: hardware requirements
> Qwen3 14B (Alibaba Qwen) is a 14.8B dense model with a 32K-token context window, released under the Apache 2.0 license. It needs about 11GB of memory at Q4_K_M with 8K context.
Qwen3 14B was the sweet spot of the Qwen3 dense lineup for 12GB and 16GB GPUs, with the same switchable thinking mode as its siblings. The Qwen3.5 generation dropped the 14B size: Qwen3.5 9B now does as well or better in less memory, and Gemma 4 26B A4B is the stronger option on 16GB cards. At Q4_K_M it needs about 11GB with 8K context, which is tight on a 12GB card and comfortable on 16GB. Native context is 32K, extendable to 128K with YaRN.

## Recommended hardware
- **Minimum GPU:** RTX 3060 12GB (12GB), Q4_K_M, ~28 tokens/sec
- **Best value:** RTX 4060 Ti 16GB (16GB), Q5_K_M, ~21 tokens/sec
- **Fastest single GPU:** RTX 5090 (32GB), Q8_0, ~84 tokens/sec
- **Mac / unified memory:** Ryzen AI Max+ 395 (128GB), Q8_0, ~12 tokens/sec

## Memory by quantization
| Quant | Weights | Total @ 8K ctx | Total @ 32K ctx |
| --- | --- | --- | --- |
| Q2_K | 5.6GB | 7.7GB | 11.7GB |
| Q3_K_M | 7.2GB | 9.4GB | 13.4GB |
| Q4_K_M | 9.0GB | 11.2GB | 15.2GB |
| Q5_K_M | 10.6GB | 12.8GB | 16.9GB |
| Q6_K | 12.2GB | 14.6GB | 18.6GB |
| Q8_0 | 15.7GB | 18.1GB | 22.1GB |
| BF16 | 29.6GB | 32.4GB | 36.4GB |

## GPUs that run it fully in memory (fastest first)
| GPU | Memory | Quant | Speed | Price |
| --- | --- | --- | --- | --- |
| RTX 5090 | 32GB | Q8_0 | ~84 tok/s | ~$4,300 |
| RTX PRO 6000 | 96GB | Q8_0 | ~84 tok/s | ~$16,000 |
| RTX 5080 | 16GB | Q5_K_M | ~68 tok/s | ~$1,600 |
| RTX PRO 5000 72GB | 72GB | Q8_0 | ~64 tok/s | ~$9,100 |
| RTX 5070 Ti | 16GB | Q5_K_M | ~63 tok/s | ~$1,200 |
| RTX 5070 | 12GB | Q4_K_M | ~56 tok/s | ~$800 |
| RTX 4080 Super | 16GB | Q5_K_M | ~53 tok/s | ~$1,150 used |
| RTX 4090 | 24GB | Q8_0 | ~49 tok/s | ~$2,500 used |
| RTX 4070 Ti Super | 16GB | Q5_K_M | ~48 tok/s | ~$900 used |
| RTX 6000 Ada | 48GB | Q8_0 | ~47 tok/s | ~$4,750 used |
| RTX 4070 | 12GB | Q4_K_M | ~43 tok/s | ~$560 used |
| RTX 3090 | 24GB | Q8_0 | ~41 tok/s | ~$1,050 used |
| M5 Ultra 512GB | 512GB | Q8_0 | ~40 tok/s | n/a |
| M5 Ultra 256GB | 256GB | Q8_0 | ~40 tok/s | ~$9,499 |
| RX 7900 XTX | 24GB | Q8_0 | ~38 tok/s | ~$1,050 used |
| RX 9070 XT | 16GB | Q5_K_M | ~37 tok/s | ~$800 |
| V100 32GB | 32GB | Q8_0 | ~36 tok/s | ~$700 used |
| RTX A6000 | 48GB | Q8_0 | ~34 tok/s | ~$4,400 used |
| RTX 5060 Ti 16GB | 16GB | Q5_K_M | ~33 tok/s | ~$620 |
| RTX 3060 12GB | 12GB | Q4_K_M | ~28 tok/s | ~$270 used |
| MI50 32GB | 32GB | Q8_0 | ~28 tok/s | ~$600 used |
| M3 Ultra 512GB | 512GB | Q8_0 | ~28 tok/s | $9,499 MSRP |
| Arc B580 | 12GB | Q4_K_M | ~26 tok/s | ~$330 |
| R9700 | 32GB | Q8_0 | ~25 tok/s | ~$1,800 |
| M5 Max 128GB | 128GB | Q8_0 | ~24 tok/s | ~$5,100 |

## How to run it
- Ollama: `ollama run qwen3:14b`
- llama.cpp: `llama-server -hf Qwen/Qwen3-14B-GGUF:Q4_K_M -c 8192 -ngl 99`
- MLX (Mac): `mlx_lm.chat --model mlx-community/Qwen3-14B-4bit`
- Weights: https://huggingface.co/Qwen/Qwen3-14B

## Strengths
- Toggleable thinking mode with solid math and coding for its size
- Fits 12GB GPUs at Q4_K_M and 16GB GPUs with room for longer context
- Apache 2.0 license

## Weaknesses
- Superseded: there is no Qwen3.5 14B, and Qwen3.5 9B is as capable in less memory
- Native context is only 32K tokens
- Text only
Full page: https://llamabuilds.ai/models/qwen3-14b · Hardware guide: https://llamabuilds.ai/models/qwen3-14b/requirements

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Source: https://llamabuilds.ai/models/qwen3-14b · Updated October 14, 2026 · Llama Builds. Speeds marked ~ are estimates from our bandwidth model (https://llamabuilds.ai/methodology); measured results are labeled as such.