Can I Run Qwen3 4B Instruct 2507 on a NVIDIA RTX 3080 Ti?
Runs at full precision (fp16). Zero quality loss.
13 quantizations fit your 12.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 9.0 GB | 10.5 GB | 8.1 GB | +3.0 GB |
| Q8_0 | 5.3 GB | 6.8 GB | 4.3 GB | +6.8 GB |
| Q6_K | 4.3 GB | 5.8 GB | 3.3 GB | +7.7 GB |
| Q5_K_M | 3.8 GB | 5.3 GB | 2.9 GB | +8.2 GB |
| Q5_K_S | 3.8 GB | 5.3 GB | 2.8 GB | +8.2 GB |
| Q4_1 | 3.5 GB | 5.0 GB | 2.6 GB | +8.5 GB |
| Q4_K_M | 3.4 GB | 4.9 GB | 2.5 GB | +8.6 GB |
| Q4_K_S | 3.3 GB | 4.8 GB | 2.4 GB | +8.7 GB |
| Q4_0 | 3.3 GB | 4.8 GB | 2.4 GB | +8.8 GB |
| Q3_K_L | 2.8 GB | 4.3 GB | 2.2 GB | +9.2 GB |
| Q3_K_M | 2.7 GB | 4.2 GB | 2.1 GB | +9.3 GB |
| Q3_K_S | 2.5 GB | 4.0 GB | 1.9 GB | +9.5 GB |
| Q2_K | 2.3 GB | 3.8 GB | 1.7 GB | +9.7 GB |
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Top-ranked open-source models that fit in 12.0GB.
FAQ
Can the NVIDIA RTX 3080 Ti run Qwen3 4B Instruct 2507?
Yes. The NVIDIA RTX 3080 Ti's 12.0GB of VRAM is enough to run Qwen3 4B Instruct 2507 at fp16 quantization (9.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 12.0GB. It uses about 9.0GB of memory and 10.5GB 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.