Can Ministral 3 14B run on MacBook Pro M3 Pro 36GB?

YES — Runs Great

A85Great

Estimated from fit model

Ministral 3 14B needs ~16.7 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~14 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) — 16.7 GB, 13.8 tok/s, Runs well

16.7 GB required25.9 GB available

64% VRAM used

Fit status

Runs well

Decode

13.8 tok/s

TTFT

14046 ms

Safe context

77K

Memory

16.7 GB / 25.9 GB

Memory breakdown

Weights8.5 GB

KV Cache2.4 GB

Runtime1.8 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsMinistral 3 14B on MacBook Pro M3 Pro 36GB
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: 13.8 tok/s decode · 14.0s TTFT (warm) · 35 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	13.8 tok/s	7661 ms	77K
Coding	A	Runs well	13.8 tok/s	14046 ms	77K
Agentic Coding	S	Runs well	13.8 tok/s	20430 ms	77K
Reasoning	A	Runs well	13.8 tok/s	16599 ms	77K
RAG	S	Runs well	13.8 tok/s	25537 ms	77K

Quantization options

How Ministral 3 14B (14B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A80
Q3_K_S	3	6.9 GB	Low	A81
NVFP4	4	7.8 GB	Medium	A81
Q4_K_M	4	8.5 GB	Medium	A82
Q5_K_M	5	10.1 GB	High	A83
Q6_K	6	11.5 GB	High	A84
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 M3 Pro 36GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	16.6 tok/s
Qwen 3.5 27B	27B	S	7.2 tok/s
Qwen 3.6 27B	27B	S	7.2 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	17.1 tok/s
Qwen 3.5 35B A3B	35B	A	12.1 tok/s

Frequently asked questions

Can MacBook Pro M3 Pro 36GB run Ministral 3 14B?

Yes, MacBook Pro M3 Pro 36GB can run Ministral 3 14B with a A grade (Runs well). Expected decode speed: 13.8 tok/s.

How much VRAM does Ministral 3 14B need?

Ministral 3 14B (14B parameters) requires approximately 16.7 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 MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, Ministral 3 14B achieves approximately 13.8 tokens per second decode speed with a time-to-first-token of 14046ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 36GB run Ministral 3 14B for coding?

For coding workloads, Ministral 3 14B on MacBook Pro M3 Pro 36GB receives a A grade with 13.8 tok/s and 77K context.

What context window can Ministral 3 14B use on MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, Ministral 3 14B can safely use up to 77K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for Ministral 3 14B?

Not always. MacBook Pro M3 Pro 36GB 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 MacBook Pro M3 Pro 36GB See all hardware for Ministral 3 14B

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