Can Ministral 8B run on MacBook Pro M3 Pro 18GB?

YES — Runs Great

B62Good

○Estimated from fit model

Ministral 8B needs ~9.9 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~22 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

Q4_K_M (Medium quality) — 9.9 GB, 24.1 tok/s, Runs well

9.9 GB required13.0 GB available

76% VRAM used

Fit status

Runs well

Decode

24.1 tok/s

TTFT

8026 ms

Safe context

38K

Memory

9.9 GB / 13.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom1.9 GB

See how fast it feels

See how fast it feelsMinistral 8B on MacBook Pro M3 Pro 18GB
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: 24.1 tok/s decode · 8.0s TTFT (warm) · 60 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	22.4 tok/s	4706 ms	38K
Coding	B	Runs well	22.4 tok/s	8628 ms	38K
Agentic Coding	B	Tight fit	22.4 tok/s	12550 ms	38K
Reasoning	B	Runs well	22.4 tok/s	10197 ms	38K
RAG	B	Tight fit	22.4 tok/s	15687 ms	38K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B58
Q3_K_S	3	3.9 GB	Low	B59
NVFP4	4

Get started

Copy-paste commands to run Ministral 8B on your machine.

Run

ollama run ministral

Upgrade options

Hardware that runs Ministral 8B well

👁 Intel
Intel Arc A770 16GBBudget pick

560 GB/s (+410)

Raises estimated decode speed by about 130%.55.5 tok/s decode

Raises estimated decode speed by about 130%.

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

~$349 MSRP

👁 NVIDIA
RTX 5060 Ti 16GBBest value

448 GB/s (+298)

Raises estimated decode speed by about 154%.61.2 tok/s decode

Raises estimated decode speed by about 154%.