Fits comfortably
Running Gemma 4 E2B (Effective 2B) on NVIDIA GeForce RTX 3080 16GB (Mobile)
NVIDIA GeForce RTX 3080 16GB (Mobile) runs Gemma 4 E2B (Effective 2B) comfortably at Q8_0 with 12 GB of headroom for context.
Model size
2B params
Gemma 4 E2B (Effective 2B) →Memory available
Recommended quant
Q8_0
Highest quality that fits
Quick start with Ollama
1. Install
ollama pull gemma4:e2b2. Run
ollama run gemma4:e2bDefault quant in Ollama is Q4_K_M. To use a different quant, append it: gemma4:e2b-q5_K_M.
Variants and what fits
| Quantization | File size | VRAM required | Fits on NVIDIA GeForce RTX 3080 16GB (Mobile)? |
|---|---|---|---|
| Q4_K_M | 1.3 GB | 3 GB | Yes |
| Q8_0 | 2.2 GB | 4 GB | Yes |
Real benchmarks
Frequently asked
Can NVIDIA GeForce RTX 3080 16GB (Mobile) run Gemma 4 E2B (Effective 2B)?
NVIDIA GeForce RTX 3080 16GB (Mobile) runs Gemma 4 E2B (Effective 2B) comfortably at Q8_0 with 12 GB of headroom for context.
What quantization should I use?
Q8_0 is the highest-quality variant of Gemma 4 E2B (Effective 2B) that fits in 16 GB VRAM. Lower-bit quants will be smaller but lose some quality.
How fast will it be?
Measured at 99.1 tok/s on this combination in our testing.
See also: Gemma 4 E2B (Effective 2B), NVIDIA GeForce RTX 3080 16GB (Mobile), all benchmarks.
Reviewed by RunLocalAI Editorial. See our editorial policy.