text model · Llama 4 · macOS
Can I run Llama 4 Maverick on Apple M2 (16GB)?
No. Llama 4 Maverick needs ~231.7 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.
Needs ~231.7 GB even at Q4_K_M, but only ~10.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 221.2 GB: Llama 4 Maverick needs roughly 231.7 GB at Q4_K_M and Apple M2 (16GB) leaves only about 10.5 GB usable for a model. No single tracked device has enough memory; Llama 4 Maverick needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~231.7 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
Which quant fits
- Parameters
- 400B (MoE, 17B active)
- Q4_K_M size
- 226.09 GB
- Q8_0 size
- 396.57 GB
- Context
- 1000k
- Ollama tag
- llama4:128x17b
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Power draw
- ~50 W
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
What you can run instead
FAQ
Can Apple M2 (16GB) run Llama 4 Maverick?
No. Llama 4 Maverick needs ~231.7 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.
How much memory does Llama 4 Maverick need?
Apple M2 (16GB) does not have enough memory. At Q4_K_M the weights are ~226.09 GB; with KV cache and runtime overhead, budget ~231.7 GB at a 4k context. It is a Mixture-of-Experts model (400B total / 17B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Llama 4 Maverick 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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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.