Can Magistral Small 2507 run on MacBook Pro M4 32GB?

YES — Tight Fit

S87Excellent

Estimated — low-sample bucket· few comparable runs

Magistral Small 2507 needs ~21.4 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: llama.cppCapacity: 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) — 21.4 GB, 9.5 tok/s, Tight fit

21.4 GB required23.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

9.5 tok/s

TTFT

20344 ms

Safe context

27K

Memory

21.4 GB / 23.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsMagistral Small 2507 on MacBook Pro M4 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 9.5 tok/s decode · 20.3s TTFT (warm) · 24 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	9.5 tok/s	11097 ms	27K
Coding	S	Tight fit	5.9 tok/s	32804 ms	27K
Agentic Coding	S	Runs with offload (needs ~0.5 GB host RAM)	8.9 tok/s	31772 ms	27K
Reasoning	S	Tight fit	9.5 tok/s	24043 ms	27K
RAG	S	Runs with offload (needs ~0.5 GB host RAM)	8.9 tok/s	39714 ms	27K

Quantization options

How Magistral Small 2507 (24B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S91
Q3_K_S	3	11.8 GB	Low	S92
NVFP4	4	13.4 GB	Medium	S92
Q4_K_M	4	14.6 GB	Medium	S91
Q5_K_MBest for your GPU	5	17.3 GB	High	S91
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0

Get started

Copy-paste commands to run Magistral Small 2507 on your machine.

Run

ollama run magistral

Your hardware

More models your MacBook Pro M4 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	11.7 tok/s
Qwen 3.5 27B	27B	S	8.6 tok/s
Qwen 3.6 27B	27B	S	7.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	12.4 tok/s
Qwen 3.5 35B A3B	35B	A	10.2 tok/s

Frequently asked questions

Can MacBook Pro M4 32GB run Magistral Small 2507?

Yes, MacBook Pro M4 32GB can run Magistral Small 2507 with a S grade (Tight fit). Expected decode speed: 5.9 tok/s.

How much VRAM does Magistral Small 2507 need?

Magistral Small 2507 (24B parameters) requires approximately 21.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Magistral Small 2507?

The recommended quantization for Magistral Small 2507 is Q4_K_M, which balances quality and memory efficiency.

What speed will Magistral Small 2507 run at on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Magistral Small 2507 achieves approximately 5.9 tokens per second decode speed with a time-to-first-token of 32804ms using Q4_K_M quantization.

Can MacBook Pro M4 32GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on MacBook Pro M4 32GB receives a S grade with 5.9 tok/s and 27K context.

What context window can Magistral Small 2507 use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Magistral Small 2507 can safely use up to 27K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Magistral Small 2507 feels slow on MacBook Pro M4 32GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on MacBook Pro M4 32GB as fast as VRAM for Magistral Small 2507?

Not always. MacBook Pro M4 32GB 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 M4 32GB See all hardware for Magistral Small 2507

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