Can I Run Mistral 7B Instruct v0.1 on a NVIDIA GTX 1660 Super?
Runs at Q5_K_S — good quality with reasonable headroom.
9 quantizations fit your 6.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_SBEST | 6.0 GB | 7.5 GB | 5.0 GB | +0.0 GB |
| Q5_0 | 6.0 GB | 7.5 GB | 5.0 GB | +0.0 GB |
| Q4_K_M | 5.4 GB | 6.9 GB | 4.4 GB | +0.6 GB |
| Q4_K_S | 5.1 GB | 6.6 GB | 4.1 GB | +0.9 GB |
| Q4_0 | 5.0 GB | 6.5 GB | 4.1 GB | +1.0 GB |
| Q3_K_L | 4.2 GB | 5.7 GB | 3.8 GB | +1.8 GB |
| Q3_K_M | 4.0 GB | 5.5 GB | 3.5 GB | +2.0 GB |
| Q3_K_S | 3.8 GB | 5.3 GB | 3.2 GB | +2.2 GB |
| Q2_K | 3.4 GB | 4.9 GB | 3.1 GB | +2.6 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 GTX 1660 Super run Mistral 7B Instruct v0.1?
Yes. The NVIDIA GTX 1660 Super's 6.0GB of VRAM is enough to run Mistral 7B Instruct v0.1 at Q5_K_S quantization (6.0GB required).
What's the best quantization to use?
Q5_K_S 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.