Can I Run Qwen3 Coder 30B A3B Instruct on a NVIDIA RTX 4500 Ada?
Runs at Q5_K_M — good quality with reasonable headroom.
10 quantizations fit your 24.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 22.7 GB | 24.2 GB | 21.7 GB | +1.3 GB |
| Q5_K_S | 22.1 GB | 23.6 GB | 21.1 GB | +1.9 GB |
| Q4_1 | 20.1 GB | 21.6 GB | 19.2 GB | +3.9 GB |
| Q4_K_M | 19.5 GB | 21.0 GB | 18.6 GB | +4.5 GB |
| Q4_K_S | 18.5 GB | 20.0 GB | 17.5 GB | +5.5 GB |
| Q4_0 | 18.2 GB | 19.7 GB | 17.4 GB | +5.8 GB |
| Q3_K_L | 14.6 GB | 16.1 GB | 14.6 GB | +9.4 GB |
| Q3_K_M | 13.8 GB | 15.3 GB | 14.7 GB | +10.2 GB |
| Q3_K_S | 12.7 GB | 14.2 GB | 13.3 GB | +11.3 GB |
| Q2_K | 11.0 GB | 12.5 GB | 11.3 GB | +13.0 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 24.0GB.
FAQ
Can the NVIDIA RTX 4500 Ada run Qwen3 Coder 30B A3B Instruct?
Yes. The NVIDIA RTX 4500 Ada's 24.0GB of VRAM is enough to run Qwen3 Coder 30B A3B Instruct at Q5_K_M quantization (22.7GB 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 22.7GB of memory and 24.2GB 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.