Can Magistral Small 2507 run on MacBook Pro M3 Max 48GB?

YES — Runs Great

S92Excellent

Estimated from fit model

Magistral Small 2507 needs ~23.2 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

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) — 23.2 GB, 17.6 tok/s, Runs well

23.2 GB required34.6 GB available

67% VRAM used

Fit status

Runs well

Decode

17.6 tok/s

TTFT

10986 ms

Safe context

91K

Memory

23.2 GB / 34.6 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsMagistral Small 2507 on MacBook Pro M3 Max 48GB
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: 17.6 tok/s decode · 11.0s TTFT (warm) · 44 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	S	Runs well	17.6 tok/s	5992 ms	91K
Coding	S	Runs well	17.6 tok/s	10986 ms	91K
Agentic Coding	S	Runs well	17.6 tok/s	15979 ms	91K
Reasoning	S	Runs well	17.6 tok/s	12983 ms	91K
RAG	S	Runs well	17.6 tok/s	19974 ms	91K

Quantization options

How Magistral Small 2507 (24B params) fits at each quantization level on MacBook Pro M3 Max 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S87
Q3_K_S	3	11.8 GB	Low	S88
NVFP4	4	13.4 GB	Medium	S88
Q4_K_M	4	14.6 GB	Medium	S89
Q5_K_M	5	17.3 GB	High	S90
Q6_K	6	19.7 GB	High	S91
Q8_0Best for your GPU	8	25.7 GB	Very High	S90
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 M3 Max 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	36.3 tok/s
Qwen 3.5 27B	27B	S	15.7 tok/s
Qwen 3.6 27B	27B	S	12 tok/s
Qwen 3.6 35B A3B	35B	S	33.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	37.5 tok/s

Frequently asked questions

Can MacBook Pro M3 Max 48GB run Magistral Small 2507?

Yes, MacBook Pro M3 Max 48GB can run Magistral Small 2507 with a S grade (Runs well). Expected decode speed: 17.6 tok/s.

How much VRAM does Magistral Small 2507 need?

Magistral Small 2507 (24B parameters) requires approximately 23.2 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 M3 Max 48GB?

On MacBook Pro M3 Max 48GB, Magistral Small 2507 achieves approximately 17.6 tokens per second decode speed with a time-to-first-token of 10986ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 48GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on MacBook Pro M3 Max 48GB receives a S grade with 17.6 tok/s and 91K context.

What context window can Magistral Small 2507 use on MacBook Pro M3 Max 48GB?

On MacBook Pro M3 Max 48GB, Magistral Small 2507 can safely use up to 91K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 48GB as fast as VRAM for Magistral Small 2507?

Not always. MacBook Pro M3 Max 48GB 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 Max 48GB See all hardware for Magistral Small 2507

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