text model · Llama · macOS

Can I run Llama 3.3 70B on Apple M2 (16GB)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Llama 3.3 70B needs ~45.3 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.

Needs ~45.3 GB Device usable ~10.5 GB

Needs ~45.3 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 34.8 GB: Llama 3.3 70B needs roughly 45.3 GB at Q4_K_M and Apple M2 (16GB) leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs Llama 3.3 70B is the Apple M4 Max (64GB) at 64 GB. See Llama 3.3 70B on Apple M4 Max (64GB).

Q4_K_M needed: ~45.3 GB
Usable on device: ~10.5 GB
Device memory: 16 GB

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Which quant fits

Quant ladder vs ~10.5 GB usable

Q2_K

~32.1 GB

Q3_K_M

~37 GB

Q4_K_M

~45.3 GB

Q5_K_M

~52.7 GB

Q6_K

~60.2 GB

Q8_0

~77.8 GB

FP16

~142.8 GB

The line marks Apple M2 (16GB)'s ~10.5 GB budget; rungs past it are too large.

Model Llama

Parameters: 70B
Q4_K_M size: 42.52 GB
Q8_0 size: 74.98 GB
Context: 128k
Ollama tag: llama3.3:70b

Full Llama 3.3 70B requirements →

Device macOS

Memory: 16 GB unified
Usable for weights: ~10.5 GB
Power draw: ~50 W
Best runtime: Ollama (llama.cpp Metal backend) / MLX

Best models for Apple M2 (16GB) →

What you can run instead

Run Llama 3.3 70B on other hardware

Apple M4 Max (64GB)Apple M4 Max (128GB)AMD Ryzen AI Halo (128GB)Apple M5 Max (128GB)Apple M3 Ultra (256GB)

FAQ

Can Apple M2 (16GB) run Llama 3.3 70B?

No. Llama 3.3 70B needs ~45.3 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.

How much memory does Llama 3.3 70B need?

Apple M2 (16GB) does not have enough memory. 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.

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Markdown

[![Llama 3.3 70B on Apple M2 (16GB)](https://localmodel.run/badge/llama-3.3-70b/apple-m2-16gb.svg)](https://localmodel.run/can-i-run/llama-3.3-70b/apple-m2-16gb)

Sources

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.