Can Ministral 3 14B run on MacBook Pro M4 32GB?

YES — Runs Great

A85Great

○Estimated from fit model

Ministral 3 14B needs ~16.2 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: TransformersCapacity: 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

Q4_K_M (Medium quality) — 16.2 GB, 9.6 tok/s, Runs well

16.2 GB required23.0 GB available

70% VRAM used

Fit status

Runs well

Decode

9.6 tok/s

TTFT

20228 ms

Safe context

61K

Memory

16.2 GB / 23.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.4 GB

Runtime1.8 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsMinistral 3 14B on MacBook Pro 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: 9.6 tok/s decode · 20.2s TTFT (warm) · 24 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	9.6 tok/s	11034 ms	61K
Coding	A	Runs well	9.6 tok/s	20228 ms	61K
Agentic Coding	A	Runs well	9.6 tok/s	29423 ms	61K
Reasoning	A	Runs well	9.6 tok/s	23906 ms	61K
RAG	A	Runs well	9.6 tok/s	36779 ms	61K

Quantization options

How Ministral 3 14B (14B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A81
Q3_K_S	3	6.9 GB	Low	A82
NVFP4	4	7.8 GB	Medium	A83
Q4_K_M	4	8.5 GB	Medium	A83
Q5_K_M	5	10.1 GB	High	A84
Q6_K	6	11.5 GB	High	S85
Q8_0Best for your GPU	8	15.0 GB	Very High	A85
F16	16	28.7 GB	Maximum	F0

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 MacBook Pro M4 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	A	10.4 tok/s
👁 Alibaba Qwen 3.5 27B	27B	A	7.7 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	9.4 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	A	11 tok/s
👁 Mistral Magistral Small 2507	24B	S	9.5 tok/s

Frequently asked questions

See all results for MacBook Pro M4 32GB See all hardware for Ministral 3 14B

URL: https://willitrunai.com/can-run/ministral-3-14b-on-m4-32gb

⇱ Ministral 3 14B on MacBook Pro M4 32GB? YES