Can I Run Qwen3 VL 4B Thinking on a NVIDIA RTX 4070 Ti Super?
Runs at full precision (fp16). Zero quality loss.
12 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 9.8 GB | 11.3 GB | 0.8 GB | +6.2 GB |
| Q8_0 | 5.7 GB | 7.2 GB | 4.3 GB | +10.3 GB |
| Q6_K | 4.6 GB | 6.1 GB | 3.3 GB | +11.4 GB |
| Q5_K_M | 4.1 GB | 5.6 GB | 2.9 GB | +11.9 GB |
| Q5_K_S | 4.0 GB | 5.5 GB | 2.8 GB | +12.0 GB |
| Q4_1 | 3.8 GB | 5.3 GB | 2.6 GB | +12.3 GB |
| Q4_K_M | 3.7 GB | 5.2 GB | 2.5 GB | +12.3 GB |
| Q4_K_S | 3.5 GB | 5.0 GB | 2.4 GB | +12.5 GB |
| Q4_0 | 3.5 GB | 5.0 GB | 2.4 GB | +12.5 GB |
| Q3_K_M | 2.8 GB | 4.3 GB | 2.1 GB | +13.2 GB |
| Q3_K_S | 2.7 GB | 4.2 GB | 1.9 GB | +13.3 GB |
| Q2_K | 2.5 GB | 4.0 GB | 1.7 GB | +13.6 GB |
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Top-ranked open-source models that fit in 16.0GB.
FAQ
Can the NVIDIA RTX 4070 Ti Super run Qwen3 VL 4B Thinking?
Yes. The NVIDIA RTX 4070 Ti Super's 16.0GB of VRAM is enough to run Qwen3 VL 4B Thinking at fp16 quantization (9.8GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 16.0GB. It uses about 9.8GB of memory and 11.3GB 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.