Can Mistral Small 3.2 24B run on MacBook Pro M2 Pro 32GB?

YES — Tight Fit

A81Great

Estimated from fit model

Mistral Small 3.2 24B needs ~21.7 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: 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) — 21.7 GB, 10.3 tok/s, Tight fit

21.7 GB required23.0 GB available

94% VRAM used

Fit status

Tight fit

Decode

10.3 tok/s

TTFT

18833 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 M2 Pro 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: 10.3 tok/s decode · 18.8s TTFT (warm) · 26 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	10.3 tok/s	10272 ms	25K
Coding	A	Tight fit	10.3 tok/s	18833 ms	25K
Agentic Coding	A	Runs with offload (needs ~0.7 GB host RAM)	9.4 tok/s	29996 ms	25K
Reasoning	A	Tight fit	10.3 tok/s	22257 ms	25K
RAG	A	Runs with offload (needs ~0.7 GB host RAM)	9.4 tok/s	37495 ms	25K

Inference speed

Mistral Small 3.2 24B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Mistral Small 3.2 24B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~88 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	88.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	56.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	50.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	48.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	40.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	36.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	36.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	21.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	7.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.7	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Mistral Small 3.2 24B (24B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A83
Q3_K_S	3	11.8 GB	Low	A84
NVFP4	4	13.4 GB	Medium	A84
Q4_K_M	4	14.6 GB	Medium	A84
Q5_K_MBest for your GPU	5	17.3 GB	High	A83
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 Mistral Small 3.2 24B on your machine.

Run

ollama run mistral-small3.2

Your hardware

More models your MacBook Pro M2 Pro 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	18.7 tok/s
Qwen 3.5 27B	27B	A	8.3 tok/s
Qwen 3.6 27B	27B	S	9.2 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	19.7 tok/s
Qwen 3.5 35B A3B	35B	A	16.3 tok/s

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run Mistral Small 3.2 24B?

Yes, MacBook Pro M2 Pro 32GB can run Mistral Small 3.2 24B with a A grade (Tight fit). Expected decode speed: 10.3 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 M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Mistral Small 3.2 24B achieves approximately 10.3 tokens per second decode speed with a time-to-first-token of 18833ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 32GB run Mistral Small 3.2 24B for coding?

For coding workloads, Mistral Small 3.2 24B on MacBook Pro M2 Pro 32GB receives a A grade with 10.3 tok/s and 25K context.

What context window can Mistral Small 3.2 24B use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 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 M2 Pro 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 Pro 32GB as fast as VRAM for Mistral Small 3.2 24B?

Not always. MacBook Pro M2 Pro 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 Pro 32GB See all hardware for Mistral Small 3.2 24B

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