Can I Run Qwen3 Next 80B A3B Instruct (free) on a NVIDIA H200?
Runs at Q4_K_M — good quality with reasonable headroom.
6 quantizations fit your 141GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_K_MBEST | 50.3 GB | 51.8 GB | 48.5 GB | +90.7 GB |
| Q4_K_S | 47.5 GB | 49.0 GB | 45.5 GB | +93.5 GB |
| Q4_0 | 46.7 GB | 48.2 GB | 45.3 GB | +94.3 GB |
| Q3_K_M | 35.0 GB | 36.5 GB | 38.3 GB | +106.0 GB |
| Q3_K_S | 32.3 GB | 33.8 GB | 34.6 GB | +108.7 GB |
| Q2_K | 27.7 GB | 29.2 GB | 29.2 GB | +113.3 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 141GB.
FAQ
Can the NVIDIA H200 run Qwen3 Next 80B A3B Instruct (free)?
Yes. The NVIDIA H200's 141GB of VRAM is enough to run Qwen3 Next 80B A3B Instruct (free) at Q4_K_M quantization (50.3GB required).
What's the best quantization to use?
Q4_K_M is the highest-precision quantization that fits in your 141GB. It uses about 50.3GB of memory and 51.8GB 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.