Can I Run Qwen3 30B A3B Thinking 2507 on a NVIDIA A100 80GB?
Runs at Q8_0 — near-lossless quality.
11 quantizations fit your 80.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 33.4 GB | 34.9 GB | 32.5 GB | +46.6 GB |
| Q6_K | 26.1 GB | 27.6 GB | 25.1 GB | +53.9 GB |
| Q5_K_M | 22.7 GB | 24.2 GB | 21.7 GB | +57.3 GB |
| Q5_K_S | 22.1 GB | 23.6 GB | 21.1 GB | +58.0 GB |
| Q4_1 | 20.1 GB | 21.6 GB | 19.2 GB | +59.9 GB |
| Q4_K_M | 19.5 GB | 21.0 GB | 18.6 GB | +60.5 GB |
| Q4_K_S | 18.5 GB | 20.0 GB | 17.5 GB | +61.5 GB |
| Q4_0 | 18.2 GB | 19.7 GB | 17.4 GB | +61.8 GB |
| Q3_K_M | 13.8 GB | 15.3 GB | 14.7 GB | +66.2 GB |
| Q3_K_S | 12.7 GB | 14.2 GB | 13.3 GB | +67.3 GB |
| Q2_K | 11.0 GB | 12.5 GB | 11.3 GB | +69.0 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 80.0GB.
FAQ
Can the NVIDIA A100 80GB run Qwen3 30B A3B Thinking 2507?
Yes. The NVIDIA A100 80GB's 80.0GB of VRAM is enough to run Qwen3 30B A3B Thinking 2507 at Q8_0 quantization (33.4GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 80.0GB. It uses about 33.4GB of memory and 34.9GB 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.