text model · llama · macOS
Can I run Llama 3.3 70B on Apple M3 Ultra (256GB)?
Yes. Llama 3.3 70B runs on Apple M3 Ultra (256GB) at Q4_K_M (~45.3 GB of ~192 GB usable).
Runs at Q4_K_M using ~45.3 GB of ~192 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~45.3 GB
- Usable on device
- ~192 GB
- Device memory
- 256 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run llama3.3:70b llama-cli -hf bartowski/Llama-3.3-70B-Instruct-GGUF:Q4_K_M lms get bartowski/Llama-3.3-70B-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
- 70B
- Q4_K_M size
- 42.52 GB
- Q8_0 size
- 74.98 GB
- Context
- 128k
- Ollama tag
- llama3.3:70b
- Memory
- 256 GB unified
- Usable for weights
- ~192 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Llama 3.3 70B on other hardware
FAQ
Can Apple M3 Ultra (256GB) run Llama 3.3 70B?
Yes. Llama 3.3 70B runs on Apple M3 Ultra (256GB) at Q4_K_M (~45.3 GB of ~192 GB usable).
How much memory does Llama 3.3 70B need?
Apple M3 Ultra (256GB) has room to spare. At Q4_K_M the weights are ~42.52 GB; with KV cache and runtime overhead, budget ~45.3 GB at a 4k context.
What is the best tool to run Llama 3.3 70B 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.