Can I Run LFM2 1.5B on a NVIDIA GTX 1650?
Yes
Runs at full precision (fp16). Zero quality loss.
Model size
1.5B
GPU memory
4.0GB
Smallest quant
Q3_K_S
Best fit
fp16
13 quantizations fit your 4.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 4.0 GB | 5.5 GB | 2.3 GB | 0 |
| Q8_0 | 2.6 GB | 4.1 GB | 1.3 GB | +1.4 GB |
| Q5_1 | 2.1 GB | 3.6 GB | 0.9 GB | +1.9 GB |
| Q5_K_M | 2.1 GB | 3.6 GB | 0.9 GB | +1.9 GB |
| Q5_K_S | 2.0 GB | 3.5 GB | 0.8 GB | +2.0 GB |
| Q5_0 | 2.0 GB | 3.5 GB | 0.8 GB | +2.0 GB |
| Q4_1 | 1.9 GB | 3.4 GB | 0.7 GB | +2.1 GB |
| Q4_K_M | 1.9 GB | 3.4 GB | 0.7 GB | +2.1 GB |
| Q4_K_S | 1.9 GB | 3.4 GB | 0.7 GB | +2.1 GB |
| Q4_0 | 1.8 GB | 3.3 GB | 0.7 GB | +2.2 GB |
| Q3_K_L | 1.7 GB | 3.2 GB | 0.7 GB | +2.3 GB |
| Q3_K_M | 1.6 GB | 3.1 GB | 0.6 GB | +2.4 GB |
| Q3_K_S | 1.6 GB | 3.1 GB | 0.6 GB | +2.4 GB |
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Full model details
LFM2 1.5B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA GTX 1650 →
Top-ranked open-source models that fit in 4.0GB.
FAQ
Can the NVIDIA GTX 1650 run LFM2 1.5B?
Yes. The NVIDIA GTX 1650's 4.0GB of VRAM is enough to run LFM2 1.5B at fp16 quantization (4.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 4.0GB. It uses about 4.0GB of memory and 5.5GB 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.