Can I Run gemma 4 E2B it on a NVIDIA RTX 3080 Ti?
Runs at full precision (fp16). Zero quality loss.
11 quantizations fit your 12.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 11.2 GB | 12.7 GB | 1.0 GB | +0.8 GB |
| Q8_0 | 6.4 GB | 7.9 GB | 5.0 GB | +5.6 GB |
| Q6_K | 5.2 GB | 6.7 GB | 4.5 GB | +6.8 GB |
| Q5_K_M | 4.6 GB | 6.1 GB | 3.4 GB | +7.4 GB |
| Q5_K_S | 4.5 GB | 6.0 GB | 3.3 GB | +7.5 GB |
| Q4_1 | 4.2 GB | 5.7 GB | 3.1 GB | +7.8 GB |
| Q4_K_M | 4.1 GB | 5.6 GB | 3.1 GB | +7.9 GB |
| Q4_K_S | 3.9 GB | 5.4 GB | 3.0 GB | +8.1 GB |
| Q4_0 | 3.9 GB | 5.4 GB | 3.0 GB | +8.1 GB |
| Q3_K_M | 3.1 GB | 4.6 GB | 2.5 GB | +8.9 GB |
| Q3_K_S | 3.0 GB | 4.5 GB | 2.5 GB | +9.0 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 12.0GB.
FAQ
Can the NVIDIA RTX 3080 Ti run gemma 4 E2B it?
Yes. The NVIDIA RTX 3080 Ti's 12.0GB of VRAM is enough to run gemma 4 E2B it at fp16 quantization (11.2GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 12.0GB. It uses about 11.2GB of memory and 12.7GB 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.