text model · Gemma · macOS
Can I run Gemma 4 E2B on Apple M5 Pro (48GB)?
Yes. Gemma 4 E2B runs on Apple M5 Pro (48GB) at Q4_K_M (~4.4 GB of ~32 GB usable).
Runs at Q4_K_M using ~4.4 GB of ~32 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M5 Pro (48GB) leaves ~27.6 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~4.4 GB
- Usable on device
- ~32 GB
- Device memory
- 48 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:e2b llama-cli -hf unsloth/gemma-4-E2B-it-GGUF:Q4_K_M lms get unsloth/gemma-4-E2B-it-GGUF - Parameters
- 5.1B
- Q4_K_M size
- 3.11 GB
- Q8_0 size
- 5.05 GB
- Context
- 128k
- Ollama tag
- gemma4:e2b
- Memory
- 48 GB unified
- Usable for weights
- ~32 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Gemma 4 E2B on other hardware
FAQ
Can Apple M5 Pro (48GB) run Gemma 4 E2B?
Yes. Gemma 4 E2B runs on Apple M5 Pro (48GB) at Q4_K_M (~4.4 GB of ~32 GB usable).
How much memory does Gemma 4 E2B need?
Apple M5 Pro (48GB) has room to spare. At Q4_K_M the weights are ~3.11 GB; with KV cache and runtime overhead, budget ~4.4 GB at a 4k context.
What is the best tool to run Gemma 4 E2B 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-4-e2b/apple-m5-pro-48gb) 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.