text model · Gemma · Windows
Can I run Gemma 3n E4B on Nvidia GeForce RTX 2060 (6GB)?
No. Gemma 3n E4B needs ~5.7 GB even at Q4_K_M, but Nvidia GeForce RTX 2060 (6GB) only has ~5 GB usable.
Needs ~5.7 GB even at Q4_K_M, but only ~5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 0.7 GB: Gemma 3n E4B needs roughly 5.7 GB at Q4_K_M and Nvidia GeForce RTX 2060 (6GB) leaves only about 5 GB usable for a model. The lightest tracked hardware that runs Gemma 3n E4B is the Nvidia GeForce RTX 3060 Ti (8GB) at 8 GB. See Gemma 3n E4B on Nvidia GeForce RTX 3060 Ti (8GB).
- Q4_K_M needed
- ~5.7 GB
- Usable on device
- ~5 GB
- Device memory
- 6 GB
Which quant fits
- Parameters
- 8B
- Q4_K_M size
- 4.23 GB
- Q8_0 size
- 6.85 GB
- Context
- 32k
- Ollama tag
- gemma3n:e4b
- Memory
- 6 GB vram
- Usable for weights
- ~5 GB
- Power draw
- ~160 W
- Best runtime
- Ollama (CUDA) / llama.cpp CUDA
What you can run instead
Run Gemma 3n E4B on other hardware
FAQ
Can Nvidia GeForce RTX 2060 (6GB) run Gemma 3n E4B?
No. Gemma 3n E4B needs ~5.7 GB even at Q4_K_M, but Nvidia GeForce RTX 2060 (6GB) only has ~5 GB usable.
How much memory does Gemma 3n E4B need?
Nvidia GeForce RTX 2060 (6GB) does not have enough memory. At Q4_K_M the weights are ~4.23 GB; with KV cache and runtime overhead, budget ~5.7 GB at a 4k context.
What is the best tool to run Gemma 3n E4B on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/gemma-3n-e4b/nvidia-rtx-2060-6gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.