text model · Llama 3.2 Vision · macOS

Can I run Llama 3.2 Vision 11B on Apple M1 (8GB)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Llama 3.2 Vision 11B needs ~9 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.

Needs ~9 GB Device usable ~5.5 GB

Needs ~9 GB even at Q4_K_M, but only ~5.5 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 3.5 GB: Llama 3.2 Vision 11B needs roughly 9 GB at Q4_K_M and Apple M1 (8GB) leaves only about 5.5 GB usable for a model. The lightest tracked hardware that runs Llama 3.2 Vision 11B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Llama 3.2 Vision 11B on Nvidia GeForce RTX 3060 (12GB).

Q4_K_M needed: ~9 GB
Usable on device: ~5.5 GB
Device memory: 8 GB

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Which quant fits

Quant ladder vs ~5.5 GB usable

Q2_K

~6.1 GB

Q3_K_M

~6.8 GB

Q4_K_M

~9 GB

Q5_K_M

~9.2 GB

Q6_K

~10.4 GB

Q8_0

~13.1 GB

FP16

~23 GB

The line marks Apple M1 (8GB)'s ~5.5 GB budget; rungs past it are too large.

Model Llama 3.2 Vision

Parameters: 10.7B
Q4_K_M size: 7.36 GB
Q8_0 size: 11.49 GB
Context: 128k
Ollama tag: llama3.2-vision:11b

Full Llama 3.2 Vision 11B requirements →

Device macOS

Memory: 8 GB unified
Usable for weights: ~5.5 GB
Power draw: ~39 W
Best runtime: Ollama (llama.cpp Metal backend)

Best models for Apple M1 (8GB) →

What you can run instead

Run Llama 3.2 Vision 11B on other hardware

Nvidia GeForce RTX 3060 (12GB)Nvidia GeForce RTX 4070 (12GB)Apple M2 (16GB)Apple M4 (16GB)Nvidia GeForce RTX 4060 Ti (16GB)Nvidia GeForce RTX 4080 (16GB)

FAQ

Can Apple M1 (8GB) run Llama 3.2 Vision 11B?

No. Llama 3.2 Vision 11B needs ~9 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.

How much memory does Llama 3.2 Vision 11B need?

Apple M1 (8GB) does not have enough memory. At Q4_K_M the weights are ~7.36 GB; with KV cache and runtime overhead, budget ~9 GB at a 4k context.

What is the best tool to run Llama 3.2 Vision 11B 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 3.2 Vision 11B on Apple M1 (8GB)](https://localmodel.run/badge/llama-3.2-vision-11b/apple-m1-8gb.svg)](https://localmodel.run/can-i-run/llama-3.2-vision-11b/apple-m1-8gb)

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.