Can I Run DeepSeek R1 Distill Llama 8B on a NVIDIA RTX 3080 Ti?
Runs at Q8_0 — near-lossless quality.
4 quantizations fit your 12.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 9.5 GB | 11.0 GB | 8.5 GB | +2.5 GB |
| Q6_K | 7.6 GB | 9.1 GB | 6.6 GB | +4.4 GB |
| Q5_K_M | 6.7 GB | 8.2 GB | 5.7 GB | +5.3 GB |
| Q4_K_M | 5.8 GB | 7.3 GB | 4.8 GB | +6.2 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 12.0GB.
FAQ
Can the NVIDIA RTX 3080 Ti run DeepSeek R1 Distill Llama 8B?
Yes. The NVIDIA RTX 3080 Ti's 12.0GB of VRAM is enough to run DeepSeek R1 Distill Llama 8B at Q8_0 quantization (9.5GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 12.0GB. It uses about 9.5GB of memory and 11.0GB 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.