Introduction: A "Scientific" Model Strong in Math and Code
Qwen 3 (formerly the Qwen 2.5 lineage) possesses performance that rivals or even surpasses GPT-4 in
mathematics, programming, and logical reasoning.
With model sizes ranging from small to large, you can select the optimal version based on your GPU.
| Model | VRAM Required (4-bit) | Recommended GPU / Use Case |
|---|---|---|
| Qwen 3 14B | 10GB - 12GB | RTX 3060/4070 (12GB) recommended. Balanced type. Sufficient for general coding and translation. |
| Qwen 3 32B (Best) | 20GB - 24GB | RTX 3090/4090 (24GB) recommended. Highly recommended. Matches 70B intelligence while running on a single consumer high-end GPU. |
| Qwen 3 72B | 40GB - 48GB | Mac Studio (64GB+) or Dual GPUs. Overwhelming performance but higher construction difficulty. |
Method A: Fastest Setup with Ollama
Environment set up with one command. Qwen updates frequently, and Ollama ensures you always get the latest version.
Run Qwen
Open PowerShell and enter the following command corresponding to your target model size.
32B Model (Recommended for RTX 4090/3090 users)
* If your GPU memory is 16GB or less:
14B Model (General/Lightweight)
Method B: Fine-Tuning with LM Studio
Useful for adjusting context length or setting a permanent system prompt.
Search for Models
Enter qwen 2.5 or qwen 3 in the LM Studio search bar.
Choose models from reliable uploaders like the official Qwen account or Bartowski.
Select Quantization Level
Choose based on your VRAM capacity.
- Q4_K_M (Recommended): Good balance of quality and speed.
- Q6_K: Improved precision if you have VRAM to spare.
- IQ3_M: Emergency option if VRAM is very tight.
Troubleshooting
Q. It responds in Chinese or Japanese?
Because Qwen’s training data contains a large amount of Chinese, it may occasionally begin responding in Chinese or default to its original Japanese settings.
Solution: Explicitly set your preference in the System Prompt.
Q. Maximizing Coding Performance
Qwen's "Coder" models are particularly excellent. For purely programming purposes, try using the code-specialized version instead of the general model.
