Can I Run Llama 3 8B Instruct on a NVIDIA RTX 2060 6GB?
Runs at Q4_1 — good quality with reasonable headroom.
8 quantizations fit your 6.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_1BEST | 6.0 GB | 7.5 GB | 5.1 GB | 0 |
| Q4_K_M | 5.8 GB | 7.3 GB | 4.9 GB | +0.2 GB |
| Q4_K_S | 5.6 GB | 7.1 GB | 4.7 GB | +0.4 GB |
| Q4_0 | 5.5 GB | 7.0 GB | 4.7 GB | +0.5 GB |
| Q3_K_L | 4.6 GB | 6.1 GB | 4.3 GB | +1.4 GB |
| Q3_K_M | 4.3 GB | 5.8 GB | 4.0 GB | +1.7 GB |
| Q3_K_S | 4.1 GB | 5.6 GB | 3.7 GB | +1.9 GB |
| Q2_K | 3.6 GB | 5.1 GB | 3.2 GB | +2.4 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 6.0GB.
FAQ
Can the NVIDIA RTX 2060 6GB run Llama 3 8B Instruct?
Yes. The NVIDIA RTX 2060 6GB's 6.0GB of VRAM is enough to run Llama 3 8B Instruct at Q4_1 quantization (6.0GB required).
What's the best quantization to use?
Q4_1 is the highest-precision quantization that fits in your 6.0GB. It uses about 6.0GB of memory and 7.5GB 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.