Can I Run / Gemma 4 E2B / on NVIDIA GTX 1650
Can I Run Gemma 4 E2B on a NVIDIA GTX 1650?
Yes
Runs at Q4_K_S — good quality with reasonable headroom.
Model size
5.1B
GPU memory
4.0GB
Smallest quant
Q2_K
Best fit
Q4_K_S
6 quantizations fit your 4.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_K_SBEST | 3.9 GB | 5.4 GB | 3.0 GB | +0.1 GB |
| Q4_0 | 3.9 GB | 5.4 GB | 3.0 GB | +0.1 GB |
| Q3_K_L | 3.3 GB | 4.8 GB | 3.3 GB | +0.7 GB |
| Q3_K_M | 3.1 GB | 4.6 GB | 2.5 GB | +0.9 GB |
| Q3_K_S | 3.0 GB | 4.5 GB | 2.5 GB | +1.0 GB |
| Q2_K | 2.7 GB | 4.2 GB | 3.0 GB | +1.3 GB |
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Full model details
Gemma 4 E2B →
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 Gemma 4 E2B?
Yes. The NVIDIA GTX 1650's 4.0GB of VRAM is enough to run Gemma 4 E2B at Q4_K_S quantization (3.9GB required).
What's the best quantization to use?
Q4_K_S is the highest-precision quantization that fits in your 4.0GB. It uses about 3.9GB of memory and 5.4GB 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.