Can I Run Qwen3 VL 32B Instruct on a NVIDIA RTX 3090 Ti?
Runs at Q4_1 — good quality with reasonable headroom.
7 quantizations fit your 24.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_1BEST | 21.9 GB | 23.4 GB | 20.6 GB | +2.1 GB |
| Q4_K_M | 21.3 GB | 22.8 GB | 19.8 GB | +2.8 GB |
| Q4_K_S | 20.1 GB | 21.6 GB | 18.8 GB | +3.9 GB |
| Q4_0 | 19.8 GB | 21.3 GB | 18.7 GB | +4.2 GB |
| Q3_K_M | 15.0 GB | 16.5 GB | 16.0 GB | +9.0 GB |
| Q3_K_S | 13.9 GB | 15.4 GB | 14.4 GB | +10.1 GB |
| Q2_K | 12.0 GB | 13.5 GB | 12.3 GB | +12.0 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 3090 Ti run Qwen3 VL 32B Instruct?
Yes. The NVIDIA RTX 3090 Ti's 24.0GB of VRAM is enough to run Qwen3 VL 32B Instruct at Q4_1 quantization (21.9GB required).
What's the best quantization to use?
Q4_1 is the highest-precision quantization that fits in your 24.0GB. It uses about 21.9GB of memory and 23.4GB 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.