Can I Run Qwen3 VL 30B A3B Thinking on a NVIDIA A100 40GB?
Runs at Q8_0 — near-lossless quality.
11 quantizations fit your 40.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 34.0 GB | 35.5 GB | 32.5 GB | +6.0 GB |
| Q6_K | 26.6 GB | 28.1 GB | 25.1 GB | +13.4 GB |
| Q5_K_M | 23.1 GB | 24.6 GB | 21.7 GB | +16.9 GB |
| Q5_K_S | 22.5 GB | 24.0 GB | 21.1 GB | +17.5 GB |
| Q4_1 | 20.4 GB | 21.9 GB | 19.2 GB | +19.6 GB |
| Q4_K_M | 19.9 GB | 21.4 GB | 18.6 GB | +20.1 GB |
| Q4_K_S | 18.8 GB | 20.3 GB | 17.5 GB | +21.2 GB |
| Q4_0 | 18.5 GB | 20.0 GB | 17.4 GB | +21.5 GB |
| Q3_K_M | 14.0 GB | 15.5 GB | 14.7 GB | +26.0 GB |
| Q3_K_S | 13.0 GB | 14.5 GB | 13.3 GB | +27.0 GB |
| Q2_K | 11.2 GB | 12.7 GB | 11.3 GB | +28.8 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 40.0GB.
FAQ
Can the NVIDIA A100 40GB run Qwen3 VL 30B A3B Thinking?
Yes. The NVIDIA A100 40GB's 40.0GB of VRAM is enough to run Qwen3 VL 30B A3B Thinking at Q8_0 quantization (34.0GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 40.0GB. It uses about 34.0GB of memory and 35.5GB 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.