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text model · mistral · macOS

Can I run Mistral Nemo 12B on Apple M5 (16GB)?

Compatibility verdict VRAM threshold engine
Yes, it runsGPU accelerated

Yes. Mistral Nemo 12B runs on Apple M5 (16GB) at Q4_K_M (~8.6 GB of ~10.5 GB usable).

Needs ~8.6 GB Device usable ~10.5 GB

Runs at Q4_K_M using ~8.6 GB of ~10.5 GB usable.

Q4_K_M needed
~8.6 GB
Usable on device
~10.5 GB
Device memory
16 GB
Best quant
Q4_K_M

Run it

Install commands macOS

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

Ollama
$ ollama run mistral-nemo:12b
llama.cpp
$ llama-cli -hf bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M
LM Studio
$ lms get bartowski/Mistral-Nemo-Instruct-2407-GGUF

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.

Model mistral
Parameters
12.2B
Q4_K_M size
6.96 GB
Q8_0 size
12.13 GB
Context
128k
Ollama tag
mistral-nemo:12b
Full Mistral Nemo 12B 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 Mistral Nemo 12B 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 Mistral Nemo 12B?

Yes. Mistral Nemo 12B runs on Apple M5 (16GB) at Q4_K_M (~8.6 GB of ~10.5 GB usable).

How much memory does Mistral Nemo 12B need?

Apple M5 (16GB) has room to spare. At Q4_K_M the weights are ~6.96 GB; with KV cache and runtime overhead, budget ~8.6 GB at a 4k context.

What is the best tool to run Mistral Nemo 12B 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.

Sources

Memory figures are estimates. See methodology.