Can I Run Qwen3 4B Thinking 2507 on a NVIDIA GTX 1650?
Runs at Q5_K_M — good quality with reasonable headroom.
10 quantizations fit your 4.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 3.8 GB | 5.3 GB | 2.9 GB | +0.2 GB |
| Q5_K_S | 3.8 GB | 5.3 GB | 2.8 GB | +0.2 GB |
| Q4_1 | 3.5 GB | 5.0 GB | 2.6 GB | +0.5 GB |
| Q4_K_M | 3.4 GB | 4.9 GB | 2.5 GB | +0.6 GB |
| Q4_K_S | 3.3 GB | 4.8 GB | 2.4 GB | +0.7 GB |
| Q4_0 | 3.3 GB | 4.8 GB | 2.4 GB | +0.8 GB |
| Q3_K_L | 2.8 GB | 4.3 GB | 2.2 GB | +1.2 GB |
| Q3_K_M | 2.7 GB | 4.2 GB | 2.1 GB | +1.3 GB |
| Q3_K_S | 2.5 GB | 4.0 GB | 1.9 GB | +1.5 GB |
| Q2_K | 2.3 GB | 3.8 GB | 1.7 GB | +1.7 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 4.0GB.
FAQ
Can the NVIDIA GTX 1650 run Qwen3 4B Thinking 2507?
Yes. The NVIDIA GTX 1650's 4.0GB of VRAM is enough to run Qwen3 4B Thinking 2507 at Q5_K_M quantization (3.8GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 4.0GB. It uses about 3.8GB of memory and 5.3GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.