Can Ministral 3 8B run on MacBook Pro M3 Pro 18GB?

YES — Tight Fit

A80Great

Estimated from fit model

Ministral 3 8B needs ~10.8 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: TransformersCapacity: TightBandwidth: 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) — 10.8 GB, 24.1 tok/s, Tight fit

10.8 GB required13.0 GB available

83% VRAM used

Fit status

Tight fit

Decode

24.1 tok/s

TTFT

8026 ms

Safe context

32K

Memory

10.8 GB / 13.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.8 GB

Headroom1.9 GB

See how fast it feels

See how fast it feelsMinistral 3 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	A	Runs well	24.1 tok/s	4378 ms	32K
Coding	A	Tight fit	24.1 tok/s	8026 ms	32K
Agentic Coding	A	Runs with offload (needs ~0 GB host RAM)	21.9 tok/s	12884 ms	32K
Reasoning	A	Tight fit	24.1 tok/s	9485 ms	32K
RAG	A	Runs with offload (needs ~0 GB host RAM)	21.9 tok/s	16106 ms	32K

Quantization options

How Ministral 3 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	A79
Q3_K_S	3	3.9 GB	Low	A80
NVFP4	4	4.5 GB	Medium	A81
Q4_K_M	4	4.9 GB	Medium	A81
Q5_K_M	5	5.8 GB	High	A83
Q6_K	6	6.6 GB	High	A83
Q8_0Best for your GPU	8	8.6 GB	Very High	A82
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Ministral 3 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Ministral-3-8B-Instruct-2512" \
  --hf-file "Ministral-3-8B-Instruct-2512-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your MacBook Pro M3 Pro 18GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	21.4 tok/s
Qwen 3 14B	14B	A	10.6 tok/s
Ministral 3 14B	14B	A	10.5 tok/s
Phi-4 14B	14B	B	9.9 tok/s
Qwen 2.5 14B	14B	B	10.1 tok/s

Frequently asked questions

Can MacBook Pro M3 Pro 18GB run Ministral 3 8B?

Yes, MacBook Pro M3 Pro 18GB can run Ministral 3 8B with a A grade (Tight fit). Expected decode speed: 24.1 tok/s.

How much VRAM does Ministral 3 8B need?

Ministral 3 8B (8B parameters) requires approximately 10.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 8B?

The recommended quantization for Ministral 3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 8B run at on MacBook Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, Ministral 3 8B achieves approximately 24.1 tokens per second decode speed with a time-to-first-token of 8026ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 18GB run Ministral 3 8B for coding?

For coding workloads, Ministral 3 8B on MacBook Pro M3 Pro 18GB receives a A grade with 24.1 tok/s and 32K context.

What context window can Ministral 3 8B use on MacBook Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, Ministral 3 8B can safely use up to 32K 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 18GB as fast as VRAM for Ministral 3 8B?

Not always. MacBook Pro M3 Pro 18GB 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 18GB See all hardware for Ministral 3 8B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/ministral-3-8b-on-m3-pro-18gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: