Can I Run Qwen3 VL 30B A3B Thinking on a NVIDIA RTX 4500 Ada?
Runs at Q5_K_M — good quality with reasonable headroom.
9 quantizations fit your 24.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 23.1 GB | 24.6 GB | 21.7 GB | +0.9 GB |
| Q5_K_S | 22.5 GB | 24.0 GB | 21.1 GB | +1.5 GB |
| Q4_1 | 20.4 GB | 21.9 GB | 19.2 GB | +3.6 GB |
| Q4_K_M | 19.9 GB | 21.4 GB | 18.6 GB | +4.1 GB |
| Q4_K_S | 18.8 GB | 20.3 GB | 17.5 GB | +5.2 GB |
| Q4_0 | 18.5 GB | 20.0 GB | 17.4 GB | +5.5 GB |
| Q3_K_M | 14.0 GB | 15.5 GB | 14.7 GB | +10.0 GB |
| Q3_K_S | 13.0 GB | 14.5 GB | 13.3 GB | +11.0 GB |
| Q2_K | 11.2 GB | 12.7 GB | 11.3 GB | +12.8 GB |
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FAQ
Can the NVIDIA RTX 4500 Ada run Qwen3 VL 30B A3B Thinking?
Yes. The NVIDIA RTX 4500 Ada's 24.0GB of VRAM is enough to run Qwen3 VL 30B A3B Thinking at Q5_K_M quantization (23.1GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 24.0GB. It uses about 23.1GB of memory and 24.6GB 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.