Can I Run OLMo 2 1124 7B Instruct on a NVIDIA RTX 2060 6GB?
Runs at Q4_K_M — good quality with reasonable headroom.
7 quantizations fit your 6.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_K_MBEST | 5.4 GB | 6.9 GB | 4.5 GB | +0.6 GB |
| Q4_K_S | 5.2 GB | 6.7 GB | 4.3 GB | +0.8 GB |
| Q4_0 | 5.1 GB | 6.6 GB | 4.2 GB | +0.9 GB |
| Q3_K_L | 4.3 GB | 5.8 GB | 4.0 GB | +1.8 GB |
| Q3_K_M | 4.1 GB | 5.6 GB | 3.6 GB | +1.9 GB |
| Q3_K_S | 3.8 GB | 5.3 GB | 3.3 GB | +2.2 GB |
| Q2_K | 3.4 GB | 4.9 GB | 2.9 GB | +2.6 GB |
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Top-ranked open-source models that fit in 6.0GB.
FAQ
Can the NVIDIA RTX 2060 6GB run OLMo 2 1124 7B Instruct?
Yes. The NVIDIA RTX 2060 6GB's 6.0GB of VRAM is enough to run OLMo 2 1124 7B Instruct at Q4_K_M quantization (5.4GB required).
What's the best quantization to use?
Q4_K_M is the highest-precision quantization that fits in your 6.0GB. It uses about 5.4GB of memory and 6.9GB 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.