Can Ministral 3 3B run on MacBook Pro M3 24GB?

YES — Runs Great

A70Great

○Estimated from fit model

Ministral 3 3B needs ~7.0 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~40 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) — 7.0 GB, 39.9 tok/s, Runs well

7.0 GB required17.3 GB available

40% VRAM used

Fit status

Runs well

Decode

39.9 tok/s

TTFT

4847 ms

Safe context

242K

Memory

7.0 GB / 17.3 GB

Memory breakdown

Weights1.8 GB

KV Cache0.7 GB

Runtime1.8 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsMinistral 3 3B on MacBook Pro M3 24GB
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: 39.9 tok/s decode · 4.8s TTFT (warm) · 100 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	B	Runs well	39.9 tok/s	2644 ms	242K
Coding	A	Runs well	39.9 tok/s	4847 ms	242K
Agentic Coding	A	Runs well	39.9 tok/s	7050 ms	242K
Reasoning	A	Runs well	39.9 tok/s	5728 ms	242K
RAG	A	Runs well	39.9 tok/s	8812 ms	242K

Quantization options

How Ministral 3 3B (3B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	B68
Q3_K_S	3	1.5 GB	Low	B68
NVFP4	4	1.7 GB	Medium	B68
Q4_K_M	4	1.8 GB	Medium	B68
Q5_K_M	5	2.2 GB	High	B68
Q6_K	6	2.5 GB	High	B68
Q8_0	8	3.2 GB	Very High	B69
F16Best for your GPU	16	6.1 GB	Maximum	A71

Get started

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

Run

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

Your hardware

More models your MacBook Pro M3 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	13.3 tok/s
👁 Alibaba Qwen 3 14B	14B	S	8.6 tok/s
👁 Alibaba Qwen 3.5 4B	4B	S	30 tok/s
👁 Alibaba Qwen 3 8B	8B	S	15 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	8.2 tok/s

Frequently asked questions

See all results for MacBook Pro M3 24GB See all hardware for Ministral 3 3B

URL: https://willitrunai.com/can-run/ministral-3-3b-on-m3-24gb

⇱ Ministral 3 3B on MacBook Pro M3 24GB? YES