text model · Mistral · macOS

Can I run Ministral 3 14B on Apple M5 (16GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs GPU accelerated ~16 tok/s est.

Yes. Ministral 3 14B runs on Apple M5 (16GB) at Q4_K_M (~9.4 GB of ~10.5 GB usable).

Needs ~9.4 GB Device usable ~10.5 GB

Runs at Q4_K_M using ~9.4 GB of ~10.5 GB usable.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M5 (16GB) leaves ~1.1 GB of headroom.

Q4_K_M needed: ~9.4 GB
Usable on device: ~10.5 GB
Device memory: 16 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~10.5 GB usable

Q2_K

~7.6 GB

Q3_K_M

~8.5 GB

Q4_K_M

~9.4 GB

Q5_K_M

~11.7 GB

Q6_K

~13.2 GB

Q8_0

~15.1 GB

FP16

~28.7 GB

The line marks Apple M5 (16GB)'s ~10.5 GB budget; rungs past it are too large.

Run it

Install commands macOS

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

Ollama

$ ollama run ministral-3:14b

llama.cpp

$ llama-cli -hf mistralai/Ministral-3-14B-Instruct-2512-GGUF:Q4_K_M

LM Studio

$ lms get mistralai/Ministral-3-14B-Instruct-2512-GGUF

Model Mistral

Parameters: 14B
Q4_K_M size: 7.67 GB
Q8_0 size: 13.37 GB
Context: 256k
Ollama tag: ministral-3:14b

Full Ministral 3 14B requirements →

Device macOS

Memory: 16 GB unified
Usable for weights: ~10.5 GB
Best runtime: MLX direct / Ollama (MLX backend)

Best models for Apple M5 (16GB) →

You could also run

Run Ministral 3 14B 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 M5 (16GB) run Ministral 3 14B?

Yes. Ministral 3 14B runs on Apple M5 (16GB) at Q4_K_M (~9.4 GB of ~10.5 GB usable).

How much memory does Ministral 3 14B need?

Apple M5 (16GB) has room to spare. At Q4_K_M the weights are ~7.67 GB; with KV cache and runtime overhead, budget ~9.4 GB at a 4k context.

What is the best tool to run Ministral 3 14B 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

[![Ministral 3 14B on Apple M5 (16GB)](https://localmodel.run/badge/ministral-3-14b/apple-m5-16gb.svg)](https://localmodel.run/can-i-run/ministral-3-14b/apple-m5-16gb)

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.