text model · Gemma · Windows
Can I run Gemma 4 31B on 8GB RAM Laptop (CPU/iGPU only)?
No. Gemma 4 31B needs ~20.5 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
Needs ~20.5 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 15.5 GB: Gemma 4 31B needs roughly 20.5 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 4 31B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Gemma 4 31B on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~20.5 GB
- Usable on device
- ~5 GB
- Device memory
- 8 GB
Which quant fits
- Parameters
- 32.7B
- Q4_K_M size
- 18.32 GB
- Q8_0 size
- 32.64 GB
- Context
- 256k
- Ollama tag
- gemma4:31b
- 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 4 31B on other hardware
FAQ
Can 8GB RAM Laptop (CPU/iGPU only) run Gemma 4 31B?
No. Gemma 4 31B needs ~20.5 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
How much memory does Gemma 4 31B need?
8GB RAM Laptop (CPU/iGPU only) does not have enough memory. At Q4_K_M the weights are ~18.32 GB; with KV cache and runtime overhead, budget ~20.5 GB at a 4k context.
What is the best tool to run Gemma 4 31B 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-4-31b/laptop-8gb) 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.