Can I Run NVIDIA Nemotron 3 Nano 4B BF16 on a NVIDIA RTX 2070 Super?
Runs at Q8_0 — near-lossless quality.
10 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 5.3 GB | 6.8 GB | 4.2 GB | +2.8 GB |
| Q6_K | 4.3 GB | 5.8 GB | 3.9 GB | +3.7 GB |
| Q5_K_M | 3.8 GB | 5.3 GB | 3.2 GB | +4.2 GB |
| Q5_K_S | 3.8 GB | 5.3 GB | 3.1 GB | +4.2 GB |
| Q4_1 | 3.5 GB | 5.0 GB | 2.7 GB | +4.5 GB |
| Q4_K_M | 3.4 GB | 4.9 GB | 2.8 GB | +4.6 GB |
| Q4_K_S | 3.3 GB | 4.8 GB | 2.8 GB | +4.7 GB |
| Q4_0 | 3.3 GB | 4.8 GB | 2.5 GB | +4.8 GB |
| Q3_K_M | 2.7 GB | 4.2 GB | 2.5 GB | +5.3 GB |
| Q3_K_S | 2.5 GB | 4.0 GB | 2.4 GB | +5.5 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 8.0GB.
FAQ
Can the NVIDIA RTX 2070 Super run NVIDIA Nemotron 3 Nano 4B BF16?
Yes. The NVIDIA RTX 2070 Super's 8.0GB of VRAM is enough to run NVIDIA Nemotron 3 Nano 4B BF16 at Q8_0 quantization (5.3GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 8.0GB. It uses about 5.3GB of memory and 6.8GB 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.