Will It Run AI

Can Magistral Small 2507 run on MacBook Pro M2 Max 32GB?

YES — Tight Fit

S90Excellent
Estimated from fit model

Magistral Small 2507 needs ~21.4 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
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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, 17.0 tok/s, Tight fit
21.4 GB required23.0 GB available
93% VRAM used

Fit status

Tight fit

Decode

17.0 tok/s

TTFT

11364 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 M2 Max 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: 17.0 tok/s decode · 11.4s TTFT (warm) · 43 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

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

WorkloadGradeFitDecodeTTFTContext
ChatSTight fit17.0 tok/s6199 ms27K
CodingSTight fit17.0 tok/s11364 ms27K
Agentic CodingSRuns with offload (needs ~0.5 GB host RAM)15.9 tok/s17748 ms27K
ReasoningSTight fit17.0 tok/s13431 ms27K
RAGSRuns with offload (needs ~0.5 GB host RAM)15.9 tok/s22185 ms27K

Quantization options

How Magistral Small 2507 (24B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowS91
Q3_K_S
3
11.8 GB
LowS92
NVFP4
4
13.4 GB
MediumS92
Q4_K_M
4
14.6 GB
MediumS91
Q5_K_MBest for your GPU
5
17.3 GB
HighS91
Q6_K
6
19.7 GB
HighF0
Q8_0
8
25.7 GB
Very HighF0
F16
16
49.2 GB
MaximumF0

Get started

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

Run

ollama run magistral

Your hardware

More models your MacBook Pro M2 Max 32GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen3-Coder 30B A3B Instruct30.5BA31.5 tok/s
AlibabaQwen 3.5 27B27BS14.1 tok/s
AlibabaQwen 3.6 27B27BS11.6 tok/s
AlibabaQwen3-VL 30B A3B Instruct30BS33.3 tok/s
AlibabaQwen 3.5 35B A3B35BA27.5 tok/s

Frequently asked questions

Can MacBook Pro M2 Max 32GB run Magistral Small 2507?

Yes, MacBook Pro M2 Max 32GB can run Magistral Small 2507 with a S grade (Tight fit). Expected decode speed: 17.0 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 M2 Max 32GB?

On MacBook Pro M2 Max 32GB, Magistral Small 2507 achieves approximately 17.0 tokens per second decode speed with a time-to-first-token of 11364ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 32GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on MacBook Pro M2 Max 32GB receives a S grade with 17.0 tok/s and 27K context.

What context window can Magistral Small 2507 use on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 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 M2 Max 32GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for Magistral Small 2507?

Not always. MacBook Pro M2 Max 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 M2 Max 32GBSee all hardware for Magistral Small 2507
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