Can Magistral 7B run on Mac mini M2 24GB?

Q: Is unified memory on Mac mini M2 24GB as fast as VRAM for Magistral 7B?

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.

YES — Runs Great

A76Great

Estimated from fit model

Magistral 7B needs ~9.7 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~15 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) — 9.7 GB, 16.4 tok/s, Runs well

9.7 GB required17.3 GB available

56% VRAM used

Fit status

Runs well

Decode

16.4 tok/s

TTFT

11831 ms

Safe context

Memory

9.7 GB / 17.3 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsMagistral 7B 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: 16.4 tok/s decode · 11.8s TTFT (warm) · 41 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	16.4 tok/s	6453 ms	8K
Coding	A	Runs well	15.2 tok/s	12718 ms	8K
Agentic Coding	A	Runs well	16.4 tok/s	17208 ms	8K
Reasoning	A	Runs well	16.4 tok/s	13982 ms	8K
RAG	A	Runs well	16.4 tok/s	21510 ms	8K

Quantization options

How Magistral 7B (7B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A74
Q3_K_S	3	3.4 GB	Low	A75
NVFP4	4

Get started

Copy-paste commands to run Magistral 7B on your machine.

Run

lms load Magistral-7B && lms server start

Your hardware

More models your Mac mini M2 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	12.7 tok/s
Magistral Small 2507	24B	B	3.7 tok/s

Frequently asked questions

Can Mac mini M2 24GB run Magistral 7B?

Yes, Mac mini M2 24GB can run Magistral 7B with a A grade (Runs well). Expected decode speed: 15.2 tok/s.

How much VRAM does Magistral 7B need?

Magistral 7B (7B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Magistral 7B?

The recommended quantization for Magistral 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Magistral 7B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Magistral 7B achieves approximately 15.2 tokens per second decode speed with a time-to-first-token of 12718ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Magistral 7B for coding?

For coding workloads, Magistral 7B on Mac mini M2 24GB receives a A grade with 15.2 tok/s and 8K context.

What context window can Magistral 7B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Magistral 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for Magistral 7B?

See all results for Mac mini M2 24GB See all hardware for Magistral 7B

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