text model · Gemma · Windows
Can I run Gemma 3n E4B on 16GB RAM Laptop (CPU/iGPU only)?
Yes. Gemma 3n E4B runs on 16GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~5.7 GB of ~12 GB usable).
Runs at Q4_K_M using ~5.7 GB of ~12 GB usable. You have room for Q8_0 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. 16GB RAM Laptop (CPU/iGPU only) leaves ~6.3 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~5.7 GB
- Usable on device
- ~12 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~28 W
- Electricity / 1M tokens
- ~$0.11
- Pays for itself after
- ~1,923M tok
At ~$0.15/kWh and the estimated ~11 tok/s, a million generated tokens costs about $0.11 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$750 16GB RAM Laptop (CPU/iGPU only) pays for itself after roughly 1,923 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run gemma3n:e4b llama-cli -hf unsloth/gemma-3n-E4B-it-GGUF:Q4_K_M lms get unsloth/gemma-3n-E4B-it-GGUF - Parameters
- 8B
- Q4_K_M size
- 4.23 GB
- Q8_0 size
- 6.85 GB
- Context
- 32k
- Ollama tag
- gemma3n:e4b
- Memory
- 16 GB ram
- Usable for weights
- ~12 GB
- Power draw
- ~28 W
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run Gemma 3n E4B on other hardware
FAQ
Can 16GB RAM Laptop (CPU/iGPU only) run Gemma 3n E4B?
Yes. Gemma 3n E4B runs on 16GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~5.7 GB of ~12 GB usable).
How much memory does Gemma 3n E4B need?
16GB RAM Laptop (CPU/iGPU only) has room to spare. 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/gemma-3n-e4b/laptop-16gb) 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.