Can I Run OLMo 2 1124 7B Instruct on a NVIDIA RTX 5050?
Runs comfortably at Q6_K — minimal quality loss.
10 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 7.0 GB | 8.5 GB | 6.0 GB | +1.0 GB |
| Q5_K_M | 6.2 GB | 7.7 GB | 5.2 GB | +1.8 GB |
| Q5_K_S | 6.0 GB | 7.5 GB | 5.1 GB | +2.0 GB |
| Q4_K_M | 5.4 GB | 6.9 GB | 4.5 GB | +2.6 GB |
| Q4_K_S | 5.2 GB | 6.7 GB | 4.3 GB | +2.8 GB |
| Q4_0 | 5.1 GB | 6.6 GB | 4.2 GB | +2.9 GB |
| Q3_K_L | 4.3 GB | 5.8 GB | 4.0 GB | +3.8 GB |
| Q3_K_M | 4.1 GB | 5.6 GB | 3.6 GB | +3.9 GB |
| Q3_K_S | 3.8 GB | 5.3 GB | 3.3 GB | +4.2 GB |
| Q2_K | 3.4 GB | 4.9 GB | 2.9 GB | +4.6 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 5050 run OLMo 2 1124 7B Instruct?
Yes. The NVIDIA RTX 5050's 8.0GB of VRAM is enough to run OLMo 2 1124 7B Instruct at Q6_K quantization (7.0GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 8.0GB. It uses about 7.0GB of memory and 8.5GB 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.