text model · Llama 3.2 Vision · macOS

Can I run Llama 3.2 Vision 11B on Apple M3 Pro (18GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs GPU accelerated ~11 tok/s est.

Yes. Llama 3.2 Vision 11B runs on Apple M3 Pro (18GB) at Q4_K_M (~9 GB of ~12 GB usable).

Needs ~9 GB Device usable ~12 GB

Runs at Q4_K_M using ~9 GB of ~12 GB usable.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M3 Pro (18GB) leaves ~3 GB of headroom.

Q4_K_M needed: ~9 GB
Usable on device: ~12 GB
Device memory: 18 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~12 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 M3 Pro (18GB)'s ~12 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.

Power draw: ~70 W
Electricity / 1M tokens: ~$0.27
Pays for itself after: ~5,648M tok

At ~$0.15/kWh and the estimated ~11 tok/s, a million generated tokens costs about $0.27 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,299 Apple M3 Pro (18GB) pays for itself after roughly 5,648 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands macOS

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama

$ ollama run llama3.2-vision:11b

llama.cpp

$ llama-cli -hf leafspark/Llama-3.2-11B-Vision-Instruct-GGUF:Q4_K_M

LM Studio

$ lms get leafspark/Llama-3.2-11B-Vision-Instruct-GGUF

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: 18 GB unified
Usable for weights: ~12 GB
Power draw: ~70 W
Best runtime: Ollama (llama.cpp Metal backend) / MLX

Best models for Apple M3 Pro (18GB) →

You could also run

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 M3 Pro (18GB) run Llama 3.2 Vision 11B?

Yes. Llama 3.2 Vision 11B runs on Apple M3 Pro (18GB) at Q4_K_M (~9 GB of ~12 GB usable).

How much memory does Llama 3.2 Vision 11B need?

Apple M3 Pro (18GB) has room to spare. 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 M3 Pro (18GB)](https://localmodel.run/badge/llama-3.2-vision-11b/apple-m3-18gb.svg)](https://localmodel.run/can-i-run/llama-3.2-vision-11b/apple-m3-18gb)

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.