Can I Run Nemotron Nano 9B V2 (free) on a NVIDIA RTX 2080 Ti?
Runs at Q8_0 — near-lossless quality.
13 quantizations fit your 11.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 10.5 GB | 12.0 GB | 9.5 GB | +0.5 GB |
| Q6_K | 8.3 GB | 9.8 GB | 9.1 GB | +2.7 GB |
| Q5_K_M | 7.3 GB | 8.8 GB | 7.1 GB | +3.7 GB |
| Q5_K_S | 7.1 GB | 8.6 GB | 6.8 GB | +3.9 GB |
| Q5_0 | 7.1 GB | 8.6 GB | 6.3 GB | +3.9 GB |
| Q4_1 | 6.6 GB | 8.1 GB | 5.8 GB | +4.4 GB |
| Q4_K_M | 6.4 GB | 7.9 GB | 6.5 GB | +4.6 GB |
| Q4_K_S | 6.1 GB | 7.6 GB | 6.2 GB | +4.9 GB |
| Q4_0 | 6.0 GB | 7.5 GB | 5.3 GB | +5.0 GB |
| Q3_K_L | 5.0 GB | 6.5 GB | 5.5 GB | +6.0 GB |
| Q3_K_M | 4.7 GB | 6.2 GB | 5.4 GB | +6.3 GB |
| Q3_K_S | 4.4 GB | 5.9 GB | 5.1 GB | +6.6 GB |
| Q2_K | 3.9 GB | 5.4 GB | 5.0 GB | +7.1 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 11.0GB.
FAQ
Can the NVIDIA RTX 2080 Ti run Nemotron Nano 9B V2 (free)?
Yes. The NVIDIA RTX 2080 Ti's 11.0GB of VRAM is enough to run Nemotron Nano 9B V2 (free) at Q8_0 quantization (10.5GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 11.0GB. It uses about 10.5GB of memory and 12.0GB 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.