text model · Gemma · Windows
Can I run Gemma 4 26B-A4B on 32GB RAM Laptop (CPU/iGPU only)?
Yes. Gemma 4 26B-A4B runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~19 GB of ~28 GB usable).
Runs at Q4_K_M using ~19 GB of ~28 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. 32GB RAM Laptop (CPU/iGPU only) leaves ~9 GB of headroom.
- Q4_K_M needed
- ~19 GB
- Usable on device
- ~28 GB
- Device memory
- 32 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run gemma4:26b-a4b llama-cli -hf bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M lms get bartowski/google_gemma-4-26B-A4B-it-GGUF - Parameters
- 26.5B (MoE, 3.8B active)
- Q4_K_M size
- 17.04 GB
- Q8_0 size
- 26.86 GB
- Context
- 256k
- Ollama tag
- gemma4:26b-a4b
- Memory
- 32 GB ram
- Usable for weights
- ~28 GB
- Power draw
- ~28 W
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run Gemma 4 26B-A4B on other hardware
FAQ
Can 32GB RAM Laptop (CPU/iGPU only) run Gemma 4 26B-A4B?
Yes. Gemma 4 26B-A4B runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~19 GB of ~28 GB usable).
How much memory does Gemma 4 26B-A4B need?
32GB RAM Laptop (CPU/iGPU only) has room to spare. At Q4_K_M the weights are ~17.04 GB; with KV cache and runtime overhead, budget ~19 GB at a 4k context. It is a Mixture-of-Experts model (26.5B total / 3.8B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Gemma 4 26B-A4B 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-4-26b-a4b/laptop-32gb) Sources
- amazon.com
- amd.com
- en.wikipedia.org
- huggingface.co/bartowski/google_gemma-4-26B-A4B-it-GGUF
- huggingface.co/bartowski/Meta-Llama-3.1-70B-Instruct-GGUF
- huggingface.co/google
- huggingface.co/unsloth
- lmstudio.ai
- notebookcheck.net
- ollama.com
- ollama.com/library/gemma4
- ollama.com/library/llama3.1:70b
- ollama.com/library/mixtral:8x7b
- walmart.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.