Can StableLM 2 12B run on Mac mini M4 32GB?

BARELY — Tight on Memory

D36Poor

▲Estimated — low-sample bucket· few comparable runs

StableLM 2 12B needs ~25.2 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q5_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 25.2 GB, 7.1 tok/s, Very compromised (needs ~0.7 GB host RAM)

25.2 GB required23.0 GB available

110% VRAM needed

2.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.7 GB host RAM)

Decode

7.1 tok/s

TTFT

27429 ms

Safe context

Memory

25.2 GB / 23.0 GB

Offload

10%

Memory breakdown

Weights8.6 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsStableLM 2 12B on Mac mini M4 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
 - Full privacy: your prompts never leave your machine
 - No per-token cost or rate limits
 - Works offline once the model is downloaded
 - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

 Model size Q4 (4-bit) Q8 (8-bit) FP16
 7B params ~4.3 GB ~7.5 GB ~14 GB
 13B params ~7.9 GB ~13.9 GB ~26 GB
 70B params ~42.7 GB ~74.9 GB ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 7.1 tok/s decode · 27.4s TTFT (warm) · 18 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	8.2 tok/s	12863 ms	4K
Coding	D	Very compromised (needs ~0.7 GB host RAM)	7.1 tok/s	27429 ms	4K
Agentic Coding	F	Too heavy	4.4 tok/s	64131 ms	4K
Reasoning	D	Very compromised (needs ~0.7 GB host RAM)	7.1 tok/s	32416 ms	4K
RAG	F	Too heavy	4.4 tok/s	80164 ms	4K

Quantization options

How StableLM 2 12B (12B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	C45
Q3_K_S	3	5.9 GB	Low	C46
NVFP4	4	6.7 GB	Medium	C47
Q4_K_M	4	7.3 GB	Medium	C47
Q5_K_M	5	8.6 GB	High	C48
Q6_K	6	9.8 GB	High	C49
Q8_0Best for your GPU	8	12.8 GB	Very High	C50
F16	16	24.6 GB	Maximum	F0

Get started

Copy-paste commands to run StableLM 2 12B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "stabilityai/stablelm-2-12b-chat" \
 --hf-file "stablelm-2-12b-chat-Q5_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options