text model · gemma · macOS
Can I run Gemma 3 12B on Apple M5 (16GB)?
Yes. Gemma 3 12B runs on Apple M5 (16GB) at Q4_K_M (~8.9 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~8.9 GB of ~10.5 GB usable.
- Q4_K_M needed
- ~8.9 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run gemma3:12b llama-cli -hf bartowski/google_gemma-3-12b-it-GGUF:Q4_K_M lms get bartowski/google_gemma-3-12b-it-GGUF 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.
- Parameters
- 12B
- Q4_K_M size
- 7.3 GB
- Q8_0 size
- 12.51 GB
- Context
- 128k
- Ollama tag
- gemma3:12b
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Gemma 3 12B on other hardware
FAQ
Can Apple M5 (16GB) run Gemma 3 12B?
Yes. Gemma 3 12B runs on Apple M5 (16GB) at Q4_K_M (~8.9 GB of ~10.5 GB usable).
How much memory does Gemma 3 12B need?
Apple M5 (16GB) has room to spare. At Q4_K_M the weights are ~7.3 GB; with KV cache and runtime overhead, budget ~8.9 GB at a 4k context.
What is the best tool to run Gemma 3 12B 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.
Sources
Memory figures are estimates. See methodology.