text model · gemma · Windows
Can I run Gemma 2 2B on Nvidia GeForce RTX 4080 (16GB)?
Yes. Gemma 2 2B runs on Nvidia GeForce RTX 4080 (16GB) at Q4_K_M (~2.9 GB of ~15 GB usable).
Runs at Q4_K_M using ~2.9 GB of ~15 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~2.9 GB
- Usable on device
- ~15 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run gemma2:2b llama-cli -hf bartowski/gemma-2-2b-it-GGUF:Q4_K_M lms get bartowski/gemma-2-2b-it-GGUF AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 2.61B
- Q4_K_M size
- 1.71 GB
- Q8_0 size
- 2.78 GB
- Context
- 8k
- Ollama tag
- gemma2:2b
- Memory
- 16 GB vram
- Usable for weights
- ~15 GB
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Gemma 2 2B on other hardware
FAQ
Can Nvidia GeForce RTX 4080 (16GB) run Gemma 2 2B?
Yes. Gemma 2 2B runs on Nvidia GeForce RTX 4080 (16GB) at Q4_K_M (~2.9 GB of ~15 GB usable).
How much memory does Gemma 2 2B need?
Nvidia GeForce RTX 4080 (16GB) has room to spare. At Q4_K_M the weights are ~1.71 GB; with KV cache and runtime overhead, budget ~2.9 GB at a 4k context.
What is the best tool to run Gemma 2 2B 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.
Sources
Memory figures are estimates. See methodology.