Can I Run Qwen3 Next 80B A3B Thinking on a NVIDIA A100 80GB?
Runs comfortably at Q6_K — minimal quality loss.
9 quantizations fit your 80.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 68.0 GB | 69.5 GB | 65.5 GB | +12.0 GB |
| Q5_K_M | 58.7 GB | 60.2 GB | 56.7 GB | +21.3 GB |
| Q5_0 | 56.9 GB | 58.4 GB | 55.0 GB | +23.1 GB |
| Q4_K_M | 50.3 GB | 51.8 GB | 48.4 GB | +29.7 GB |
| Q4_K_S | 47.5 GB | 49.0 GB | 45.5 GB | +32.5 GB |
| Q4_0 | 46.7 GB | 48.2 GB | 45.3 GB | +33.3 GB |
| Q3_K_M | 35.0 GB | 36.5 GB | 38.3 GB | +45.0 GB |
| Q3_K_S | 32.3 GB | 33.8 GB | 34.6 GB | +47.7 GB |
| Q2_K | 27.7 GB | 29.2 GB | 29.2 GB | +52.3 GB |
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Top-ranked open-source models that fit in 80.0GB.
FAQ
Can the NVIDIA A100 80GB run Qwen3 Next 80B A3B Thinking?
Yes. The NVIDIA A100 80GB's 80.0GB of VRAM is enough to run Qwen3 Next 80B A3B Thinking at Q6_K quantization (68.0GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 80.0GB. It uses about 68.0GB of memory and 69.5GB 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.