Can StableLM 2 12B run on Mac mini M4 64GB?

YES — Runs Great

C47Usable

Estimated — low-sample bucket· few comparable runs

StableLM 2 12B needs ~28.7 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q5_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 28.7 GB, 8.2 tok/s, Runs well

28.7 GB required46.1 GB available

62% VRAM used

Fit status

Runs well

Decode

8.2 tok/s

TTFT

23582 ms

Safe context

Memory

28.7 GB / 46.1 GB

Memory breakdown

Weights8.6 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsStableLM 2 12B on Mac mini M4 64GB
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: 8.2 tok/s decode · 23.6s TTFT (warm) · 21 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	8.2 tok/s	12863 ms	4K
Coding	C	Runs well	8.2 tok/s	23582 ms	4K
Agentic Coding	C	Tight fit	8.2 tok/s	34302 ms	4K
Reasoning	C	Runs well	8.2 tok/s	27870 ms	4K
RAG	C	Tight fit	8.2 tok/s	42877 ms	4K

Inference speed

StableLM 2 12B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StableLM 2 12B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~103 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q5_K_M	103.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	60.1	Fits
RTX 4090 24GB	24 GB	Q5_K_M	53.5	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	50.1	Fits
RX 7900 XTX 24GB	24 GB	Q5_K_M	47.8	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	47.5	Fits
RTX 3090 24GB	24 GB	Q5_K_M	45.3	Offloads
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	32.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	32.7	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	25.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	23.8	Fits
RTX 4080 Super 16GB	16 GB	Q5_K_M	23.4	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	20.0	Fits
RTX 4070 12GB	12 GB	Q5_K_M	8.2	Too big
RTX 3060 12GB	12 GB	Q5_K_M	4.8	Too big
RTX 4060 8GB	8 GB	Q5_K_M	3.4	Too big

Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How StableLM 2 12B (12B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	C42
Q3_K_S	3	5.9 GB	Low	C42
NVFP4	4	6.7 GB	Medium	C42
Q4_K_M	4	7.3 GB	Medium	C42
Q5_K_M	5	8.6 GB	High	C42
Q6_K	6	9.8 GB	High	C43
Q8_0	8	12.8 GB	Very High	C44
F16Best for your GPU	16	24.6 GB	Maximum	C48

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-Optionen

Hardware, die StableLM 2 12B gut ausführt

MacBook Pro M4 Max 96GBGünstige Wahl

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 299%.32.7 tok/s Dekodierung

Raises estimated decode speed by about 299%.

Adds memory headroom for longer context windows and future model growth.

ca. $2,499 MSRP

MacBook Pro M2 Max 96GBBestes Preis-Leistungs-Verhältnis

96 GB Unified (+32)400 GB/s (+280)

Raises estimated decode speed by about 206%.25.1 tok/s Dekodierung

Raises estimated decode speed by about 206%.

Adds memory headroom for longer context windows and future model growth.

ca. $3,199 MSRP

Mac Studio M3 Ultra 96GBApple-Upgrade

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 633%.60.1 tok/s Dekodierung

Raises estimated decode speed by about 633%.

Adds memory headroom for longer context windows and future model growth.

ca. $3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run StableLM 2 12B?

Yes, Mac mini M4 64GB can run StableLM 2 12B with a C grade (Runs well). Expected decode speed: 8.2 tok/s.

How much VRAM does StableLM 2 12B need?

StableLM 2 12B (12B parameters) requires approximately 28.7 GB of memory with Q5_K_M quantization.

What is the best quantization for StableLM 2 12B?

The recommended quantization for StableLM 2 12B is Q5_K_M, which balances quality and memory efficiency.

What speed will StableLM 2 12B run at on Mac mini M4 64GB?

On Mac mini M4 64GB, StableLM 2 12B achieves approximately 8.2 tokens per second decode speed with a time-to-first-token of 23582ms using Q5_K_M quantization.

Can Mac mini M4 64GB run StableLM 2 12B for coding?

For coding workloads, StableLM 2 12B on Mac mini M4 64GB receives a C grade with 8.2 tok/s and 4K context.

What context window can StableLM 2 12B use on Mac mini M4 64GB?

On Mac mini M4 64GB, StableLM 2 12B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for StableLM 2 12B?

Not always. Mac mini M4 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac mini M4 64GB See all hardware for StableLM 2 12B

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