text model · llama · macOS
Can I run Llama 3.2 3B on Apple M1 (8GB)?
Yes. Llama 3.2 3B runs on Apple M1 (8GB) at Q4_K_M (~3.2 GB of ~5.5 GB usable).
Runs at Q4_K_M using ~3.2 GB of ~5.5 GB usable. You have room for Q8_0 for higher quality.
- Q4_K_M needed
- ~3.2 GB
- Usable on device
- ~5.5 GB
- Device memory
- 8 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run llama3.2:3b llama-cli -hf unsloth/Llama-3.2-3B-Instruct-GGUF:Q4_K_M lms get unsloth/Llama-3.2-3B-Instruct-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
- 3B
- Q4_K_M size
- 2.02 GB
- Q8_0 size
- 3.42 GB
- Context
- 128k
- Ollama tag
- llama3.2:3b
- Memory
- 8 GB unified
- Usable for weights
- ~5.5 GB
- Best runtime
- Ollama (llama.cpp Metal backend)
You could also run
Run Llama 3.2 3B on other hardware
FAQ
Can Apple M1 (8GB) run Llama 3.2 3B?
Yes. Llama 3.2 3B runs on Apple M1 (8GB) at Q4_K_M (~3.2 GB of ~5.5 GB usable).
How much memory does Llama 3.2 3B need?
Apple M1 (8GB) has room to spare. At Q4_K_M the weights are ~2.02 GB; with KV cache and runtime overhead, budget ~3.2 GB at a 4k context.
What is the best tool to run Llama 3.2 3B 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.