text model · gemma · macOS
Can I run Gemma 3 27B on Apple M4 Max (64GB)?
Yes. Gemma 3 27B runs on Apple M4 Max (64GB) at Q4_K_M (~18.6 GB of ~48 GB usable).
Runs at Q4_K_M using ~18.6 GB of ~48 GB usable. You have room for Q8_0 for higher quality.
- Q4_K_M needed
- ~18.6 GB
- Usable on device
- ~48 GB
- Device memory
- 64 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run gemma3:27b llama-cli -hf bartowski/google_gemma-3-27b-it-GGUF:Q4_K_M lms get bartowski/google_gemma-3-27b-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
- 27B
- Q4_K_M size
- 16.55 GB
- Q8_0 size
- 28.71 GB
- Context
- 128k
- Ollama tag
- gemma3:27b
- Memory
- 64 GB unified
- Usable for weights
- ~48 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Gemma 3 27B on other hardware
FAQ
Can Apple M4 Max (64GB) run Gemma 3 27B?
Yes. Gemma 3 27B runs on Apple M4 Max (64GB) at Q4_K_M (~18.6 GB of ~48 GB usable).
How much memory does Gemma 3 27B need?
Apple M4 Max (64GB) has room to spare. At Q4_K_M the weights are ~16.55 GB; with KV cache and runtime overhead, budget ~18.6 GB at a 4k context.
What is the best tool to run Gemma 3 27B 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.