Can Ministral 3 8B run on Mac mini M2 24GB?

YES — Runs Great

A81Great

Estimated from fit model

Ministral 3 8B needs ~11.5 GB VRAM. Mac mini M2 24GB has 17.3 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) — 11.5 GB, 14.3 tok/s, Runs well

11.5 GB required17.3 GB available

66% VRAM used

Fit status

Runs well

Decode

14.3 tok/s

TTFT

13521 ms

Safe context

58K

Memory

11.5 GB / 17.3 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.8 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsMinistral 3 8B on Mac mini M2 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: 14.3 tok/s decode · 13.5s TTFT (warm) · 36 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	14.3 tok/s	7375 ms	58K
Coding	A	Runs well	14.3 tok/s	13521 ms	58K
Agentic Coding	A	Runs well	14.3 tok/s	19667 ms	58K
Reasoning	A	Runs well	14.3 tok/s	15979 ms	58K
RAG	A	Runs well	14.3 tok/s	24583 ms	58K

Quantization options

How Ministral 3 8B (8B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A77
Q3_K_S	3	3.9 GB	Low	A78
NVFP4	4	4.5 GB	Medium	A78
Q4_K_M	4	4.9 GB	Medium	A78
Q5_K_M	5	5.8 GB	High	A79
Q6_K	6	6.6 GB	High	A80
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 Mac mini M2 24GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	12.7 tok/s
Qwen 3 14B	14B	S	8.2 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	7.8 tok/s
GPT-OSS 20B	21B	A	9.5 tok/s
Ministral 3 14B	14B	A	8.2 tok/s

Frequently asked questions

Can Mac mini M2 24GB run Ministral 3 8B?

Yes, Mac mini M2 24GB can run Ministral 3 8B with a A grade (Runs well). Expected decode speed: 14.3 tok/s.

How much VRAM does Ministral 3 8B need?

Ministral 3 8B (8B parameters) requires approximately 11.5 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 Mac mini M2 24GB?

On Mac mini M2 24GB, Ministral 3 8B achieves approximately 14.3 tokens per second decode speed with a time-to-first-token of 13521ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Ministral 3 8B for coding?

For coding workloads, Ministral 3 8B on Mac mini M2 24GB receives a A grade with 14.3 tok/s and 58K context.

What context window can Ministral 3 8B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Ministral 3 8B can safely use up to 58K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for Ministral 3 8B?

Not always. Mac mini M2 24GB 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 Mac mini M2 24GB See all hardware for Ministral 3 8B

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