text model · Gemma · Windows
Can I run Gemma 2 9B on 8GB RAM Laptop (CPU/iGPU only)?
No. Gemma 2 9B needs ~7.3 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
Needs ~7.3 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 2.3 GB: Gemma 2 9B needs roughly 7.3 GB at Q4_K_M and 8GB RAM Laptop (CPU/iGPU only) leaves only about 5 GB usable for a model. The lightest tracked hardware that runs Gemma 2 9B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Gemma 2 9B on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~7.3 GB
- Usable on device
- ~5 GB
- Device memory
- 8 GB
Which quant fits
- Parameters
- 9B
- Q4_K_M size
- 5.76 GB
- Q8_0 size
- 9.83 GB
- Context
- 8k
- Ollama tag
- gemma2:9b
- Memory
- 8 GB ram
- Usable for weights
- ~5 GB
- Power draw
- ~15 W
- Best runtime
- Ollama (llama.cpp backend)
What you can run instead
Run Gemma 2 9B on other hardware
FAQ
Can 8GB RAM Laptop (CPU/iGPU only) run Gemma 2 9B?
No. Gemma 2 9B needs ~7.3 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
How much memory does Gemma 2 9B need?
8GB RAM Laptop (CPU/iGPU only) does not have enough memory. At Q4_K_M the weights are ~5.76 GB; with KV cache and runtime overhead, budget ~7.3 GB at a 4k context.
What is the best tool to run Gemma 2 9B 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/gemma-2-9b/laptop-8gb) Sources
- amazon.com
- en.wikipedia.org
- huggingface.co/bartowski/gemma-2-9b-it-GGUF
- huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF
- lmarena.ai
- lmstudio.ai
- notebookcheck.net
- ollama.com
- ollama.com/library/gemma2
- ollama.com/library/gemma2/tags
- ollama.com/library/llama3.2:1b
- ollama.com/library/phi3:mini
- store.acer.com
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.