Can I Run Qwen3 4B Instruct 2507 on a NVIDIA RTX 2080 Super?
Runs at Q8_0 — near-lossless quality.
12 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 5.3 GB | 6.8 GB | 4.3 GB | +2.8 GB |
| Q6_K | 4.3 GB | 5.8 GB | 3.3 GB | +3.7 GB |
| Q5_K_M | 3.8 GB | 5.3 GB | 2.9 GB | +4.2 GB |
| Q5_K_S | 3.8 GB | 5.3 GB | 2.8 GB | +4.2 GB |
| Q4_1 | 3.5 GB | 5.0 GB | 2.6 GB | +4.5 GB |
| Q4_K_M | 3.4 GB | 4.9 GB | 2.5 GB | +4.6 GB |
| Q4_K_S | 3.3 GB | 4.8 GB | 2.4 GB | +4.7 GB |
| Q4_0 | 3.3 GB | 4.8 GB | 2.4 GB | +4.8 GB |
| Q3_K_L | 2.8 GB | 4.3 GB | 2.2 GB | +5.2 GB |
| Q3_K_M | 2.7 GB | 4.2 GB | 2.1 GB | +5.3 GB |
| Q3_K_S | 2.5 GB | 4.0 GB | 1.9 GB | +5.5 GB |
| Q2_K | 2.3 GB | 3.8 GB | 1.7 GB | +5.7 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 8.0GB.
FAQ
Can the NVIDIA RTX 2080 Super run Qwen3 4B Instruct 2507?
Yes. The NVIDIA RTX 2080 Super's 8.0GB of VRAM is enough to run Qwen3 4B Instruct 2507 at Q8_0 quantization (5.3GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 8.0GB. It uses about 5.3GB of memory and 6.8GB 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.