Can I Run LFM2.5-1.2B-Instruct (free) on a NVIDIA GTX 1660 Ti?
Runs at full precision (fp16). Zero quality loss.
6 quantizations fit your 6.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 3.4 GB | 4.9 GB | 2.3 GB | +2.6 GB |
| Q8_0 | 2.3 GB | 3.8 GB | 1.3 GB | +3.7 GB |
| Q6_K | 2.0 GB | 3.5 GB | 1.0 GB | +4.0 GB |
| Q5_K_M | 1.9 GB | 3.4 GB | 0.8 GB | +4.2 GB |
| Q4_K_M | 1.7 GB | 3.2 GB | 0.7 GB | +4.3 GB |
| Q4_0 | 1.7 GB | 3.2 GB | 0.7 GB | +4.3 GB |
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Top-ranked open-source models that fit in 6.0GB.
FAQ
Can the NVIDIA GTX 1660 Ti run LFM2.5-1.2B-Instruct (free)?
Yes. The NVIDIA GTX 1660 Ti's 6.0GB of VRAM is enough to run LFM2.5-1.2B-Instruct (free) at fp16 quantization (3.4GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 6.0GB. It uses about 3.4GB of memory and 4.9GB 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.