Will It Run AI

Can Mistral Small 3.2 24B run on MacBook Pro M4 32GB?

YES — Tight Fit

A81Great
Estimated from fit model

Mistral Small 3.2 24B needs ~21.7 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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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) — 21.7 GB, 9.5 tok/s, Tight fit
21.7 GB required23.0 GB available
94% VRAM used

Fit status

Tight fit

Decode

9.5 tok/s

TTFT

20344 ms

Safe context

25K

Memory

21.7 GB / 23.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.4 GB
Runtime1.2 GB
Headroom3.5 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B 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

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
ChatATight fit9.5 tok/s11097 ms25K
CodingATight fit9.5 tok/s20344 ms25K
Agentic CodingARuns with offload (needs ~0.7 GB host RAM)8.7 tok/s32403 ms25K
ReasoningATight fit9.5 tok/s24043 ms25K
RAGARuns with offload (needs ~0.7 GB host RAM)8.7 tok/s40504 ms25K

Quantization options

How Mistral Small 3.2 24B (24B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowA83
Q3_K_S
3
11.8 GB
LowA84
NVFP4
4
13.4 GB
MediumA84
Q4_K_M
4
14.6 GB
MediumA84
Q5_K_MBest for your GPU
5
17.3 GB
HighA83
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 Mistral Small 3.2 24B on your machine.

Run

ollama run mistral-small3.2

Your hardware

More models your MacBook Pro M4 32GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen3-Coder 30B A3B Instruct30.5BA11.5 tok/s
AlibabaQwen 3.5 27B27BA8.5 tok/s
AlibabaQwen 3.6 27B27BS9.4 tok/s
AlibabaQwen3-VL 30B A3B Instruct30BA12.2 tok/s
AlibabaQwen 3.5 35B A3B35BA10.1 tok/s

Frequently asked questions

Can MacBook Pro M4 32GB run Mistral Small 3.2 24B?

Yes, MacBook Pro M4 32GB can run Mistral Small 3.2 24B with a A grade (Tight fit). Expected decode speed: 9.5 tok/s.

How much VRAM does Mistral Small 3.2 24B need?

Mistral Small 3.2 24B (24B parameters) requires approximately 21.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.2 24B?

The recommended quantization for Mistral Small 3.2 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.2 24B run at on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Mistral Small 3.2 24B achieves approximately 9.5 tokens per second decode speed with a time-to-first-token of 20344ms using Q4_K_M quantization.

Can MacBook Pro M4 32GB run Mistral Small 3.2 24B for coding?

For coding workloads, Mistral Small 3.2 24B on MacBook Pro M4 32GB receives a A grade with 9.5 tok/s and 25K context.

What context window can Mistral Small 3.2 24B use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Mistral Small 3.2 24B can safely use up to 25K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 3.2 24B feels slow on MacBook Pro M4 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 M4 32GB as fast as VRAM for Mistral Small 3.2 24B?

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 32GBSee all hardware for Mistral Small 3.2 24B
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