text model · Mistral · macOS

Can I run Mistral Nemo 12B on Apple M2 (16GB)?

Compatibility verdict VRAM threshold engine

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

Yes. Mistral Nemo 12B runs on Apple M2 (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.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M2 (16GB) leaves ~1.9 GB of headroom.

Q4_K_M needed: ~8.6 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

~6.7 GB

Q3_K_M

~7.6 GB

Q4_K_M

~8.6 GB

Q5_K_M

~10.3 GB

Q6_K

~11.6 GB

Q8_0

~13.7 GB

FP16

~26 GB

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

Running cost · estimate

Power & economics est.

Power draw: ~50 W
Electricity / 1M tokens: ~$0.19
Pays for itself after: ~3,868M tok

At ~$0.15/kWh and the estimated ~11 tok/s, a million generated tokens costs about $0.19 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,199 Apple M2 (16GB) pays for itself after roughly 3,868 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 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

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
Power draw: ~50 W
Best runtime: Ollama (llama.cpp Metal backend) / MLX

Best models for Apple M2 (16GB) →

You could also run

Run Mistral Nemo 12B on other hardware

Nvidia GeForce RTX 3060 (12GB)Nvidia GeForce RTX 4070 (12GB)Apple M4 (16GB)Nvidia GeForce RTX 4060 Ti (16GB)Nvidia GeForce RTX 4080 (16GB)16GB RAM Laptop (CPU/iGPU only)

FAQ

Can Apple M2 (16GB) run Mistral Nemo 12B?

Yes. Mistral Nemo 12B runs on Apple M2 (16GB) at Q4_K_M (~8.6 GB of ~10.5 GB usable).

How much memory does Mistral Nemo 12B need?

Apple M2 (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.

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Markdown

[![Mistral Nemo 12B on Apple M2 (16GB)](https://localmodel.run/badge/mistral-nemo-12b/apple-m2-16gb.svg)](https://localmodel.run/can-i-run/mistral-nemo-12b/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.