text model · Gemma · macOS
Can I run Gemma 3n E4B on Apple M1 (8GB)?
No. Gemma 3n E4B needs ~5.7 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
Needs ~5.7 GB even at Q4_K_M, but only ~5.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 0.2 GB: Gemma 3n E4B needs roughly 5.7 GB at Q4_K_M and Apple M1 (8GB) leaves only about 5.5 GB usable for a model. The lightest tracked hardware that runs Gemma 3n E4B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Gemma 3n E4B on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~5.7 GB
- Usable on device
- ~5.5 GB
- Device memory
- 8 GB
Which quant fits
- Parameters
- 8B
- Q4_K_M size
- 4.23 GB
- Q8_0 size
- 6.85 GB
- Context
- 32k
- Ollama tag
- gemma3n:e4b
- Memory
- 8 GB unified
- Usable for weights
- ~5.5 GB
- Power draw
- ~39 W
- Best runtime
- Ollama (llama.cpp Metal backend)
What you can run instead
Run Gemma 3n E4B on other hardware
FAQ
Can Apple M1 (8GB) run Gemma 3n E4B?
No. Gemma 3n E4B needs ~5.7 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
How much memory does Gemma 3n E4B need?
Apple M1 (8GB) does not have enough memory. 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 macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
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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-3n-e4b/apple-m1-8gb) 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.