Can I Run Qwen3 VL 8B Thinking on a NVIDIA L40?
Runs at full precision (f32). Zero quality loss.
13 quantizations fit your 48.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| f32BEST | 36.2 GB | 37.7 GB | 2.3 GB | +11.8 GB |
| fp16 | 18.6 GB | 20.1 GB | 1.2 GB | +29.4 GB |
| Q8_0 | 10.3 GB | 11.8 GB | 8.7 GB | +37.6 GB |
| Q6_K | 8.3 GB | 9.8 GB | 6.7 GB | +39.8 GB |
| Q5_K_M | 7.3 GB | 8.8 GB | 5.8 GB | +40.8 GB |
| Q5_K_S | 7.1 GB | 8.6 GB | 5.7 GB | +40.9 GB |
| Q4_1 | 6.5 GB | 8.0 GB | 5.3 GB | +41.5 GB |
| Q4_K_M | 6.3 GB | 7.8 GB | 5.0 GB | +41.7 GB |
| Q4_K_S | 6.0 GB | 7.5 GB | 4.8 GB | +42.0 GB |
| Q4_0 | 6.0 GB | 7.5 GB | 4.8 GB | +42.0 GB |
| Q3_K_M | 4.7 GB | 6.2 GB | 4.1 GB | +43.3 GB |
| Q3_K_S | 4.4 GB | 5.9 GB | 3.8 GB | +43.6 GB |
| Q2_K | 3.9 GB | 5.4 GB | 3.3 GB | +44.1 GB |
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FAQ
Can the NVIDIA L40 run Qwen3 VL 8B Thinking?
Yes. The NVIDIA L40's 48.0GB of VRAM is enough to run Qwen3 VL 8B Thinking at f32 quantization (36.2GB required).
What's the best quantization to use?
f32 is the highest-precision quantization that fits in your 48.0GB. It uses about 36.2GB of memory and 37.7GB 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.