Can Ministral 3 14B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

A84Great

Estimated from fit model

Ministral 3 14B needs ~19.7 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: TransformersCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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

Q4_K_M (Medium quality) — 19.7 GB, 58.4 tok/s, Runs well

19.7 GB required46.1 GB available

43% VRAM used

Fit status

Runs well

Decode

58.4 tok/s

TTFT

3315 ms

Safe context

189K

Memory

19.7 GB / 46.1 GB

Memory breakdown

Weights8.5 GB

KV Cache2.4 GB

Runtime1.8 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsMinistral 3 14B on Mac Studio M2 Ultra 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: 58.4 tok/s decode · 3.3s TTFT (warm) · 146 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	A	Runs well	58.4 tok/s	1808 ms	189K
Coding	A	Runs well	54.3 tok/s	3563 ms	189K
Agentic Coding	S	Runs well	58.4 tok/s	4821 ms	189K
Reasoning	A	Runs well	58.4 tok/s	3917 ms	189K
RAG	S	Runs well	58.4 tok/s	6027 ms	189K

Quantization options

How Ministral 3 14B (14B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A77
Q3_K_S	3	6.9 GB	Low	A77
NVFP4	4	7.8 GB	Medium	A78
Q4_K_M	4	8.5 GB	Medium	A78
Q5_K_M	5	10.1 GB	High	A78
Q6_K	6	11.5 GB	High	A79
Q8_0	8	15.0 GB	Very High	A80
F16Best for your GPU	16	28.7 GB	Maximum	A83

Get started

Copy-paste commands to run Ministral 3 14B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Ministral-3-14B-Instruct-2512" \
  --hf-file "Ministral-3-14B-Instruct-2512-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your Mac Studio M2 Ultra 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.2 tok/s
Qwen 3.5 27B	27B	S	30.4 tok/s
Qwen 3.6 27B	27B	S	30.5 tok/s
Qwen 3.6 35B A3B	35B	S	59 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	72.6 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run Ministral 3 14B?

Yes, Mac Studio M2 Ultra 64GB can run Ministral 3 14B with a A grade (Runs well). Expected decode speed: 54.3 tok/s.

How much VRAM does Ministral 3 14B need?

Ministral 3 14B (14B parameters) requires approximately 19.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 14B?

The recommended quantization for Ministral 3 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 14B run at on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Ministral 3 14B achieves approximately 54.3 tokens per second decode speed with a time-to-first-token of 3563ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run Ministral 3 14B for coding?

For coding workloads, Ministral 3 14B on Mac Studio M2 Ultra 64GB receives a A grade with 54.3 tok/s and 189K context.

What context window can Ministral 3 14B use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Ministral 3 14B can safely use up to 189K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for Ministral 3 14B?

Not always. Mac Studio M2 Ultra 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 Studio M2 Ultra 64GB See all hardware for Ministral 3 14B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/ministral-3-14b-on-m2-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: