Can I Run / Qwen3 VL 8B Instruct / on NVIDIA RTX 5080
Can I Run Qwen3 VL 8B Instruct on a NVIDIA RTX 5080?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
8.8B
GPU memory
16.0GB
Smallest quant
Q2_K
Best fit
Q8_0
11 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 10.3 GB | 11.8 GB | 8.7 GB | +5.7 GB |
| Q6_K | 8.3 GB | 9.8 GB | 6.7 GB | +7.8 GB |
| Q5_K_M | 7.3 GB | 8.8 GB | 5.8 GB | +8.8 GB |
| Q5_K_S | 7.1 GB | 8.6 GB | 5.7 GB | +8.9 GB |
| Q4_1 | 6.5 GB | 8.0 GB | 5.3 GB | +9.5 GB |
| Q4_K_M | 6.3 GB | 7.8 GB | 5.0 GB | +9.7 GB |
| Q4_K_S | 6.0 GB | 7.5 GB | 4.8 GB | +10.0 GB |
| Q4_0 | 6.0 GB | 7.5 GB | 4.8 GB | +10.1 GB |
| Q3_K_M | 4.7 GB | 6.2 GB | 4.1 GB | +11.3 GB |
| Q3_K_S | 4.4 GB | 5.9 GB | 3.8 GB | +11.6 GB |
| Q2_K | 3.9 GB | 5.4 GB | 3.3 GB | +12.1 GB |
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Full model details
Qwen3 VL 8B Instruct →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA RTX 5080 →
Top-ranked open-source models that fit in 16.0GB.
FAQ
Can the NVIDIA RTX 5080 run Qwen3 VL 8B Instruct?
Yes. The NVIDIA RTX 5080's 16.0GB of VRAM is enough to run Qwen3 VL 8B Instruct at Q8_0 quantization (10.3GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 16.0GB. It uses about 10.3GB of memory and 11.8GB 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.