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

YES — Tight Fit

A80Great

Estimated from fit model

Mistral Small 24B 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

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 feelsMistral Small 24B on MacBook Pro M2 Max 32GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	17.0 tok/s	6199 ms	27K
Coding	A	Tight fit	17.0 tok/s	11364 ms	27K
Agentic Coding	A	Runs with offload (needs ~0.5 GB host RAM)	15.9 tok/s	17748 ms	27K
Reasoning	A	Tight fit	17.0 tok/s	13431 ms	27K
RAG	A	Runs with offload (needs ~0.5 GB host RAM)	15.9 tok/s	22185 ms	27K

Quantization options

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

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A81
Q3_K_S	3	11.8 GB	Low	A82
NVFP4	4	13.4 GB	Medium	A82
Q4_K_M	4	14.6 GB	Medium	A82
Q5_K_MBest for your GPU	5	17.3 GB	High	A82
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 24B on your machine.

Run

ollama run mistral-small

Your hardware

More models your MacBook Pro M2 Max 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	31.5 tok/s
Qwen 3.5 27B	27B	S	14.1 tok/s
Qwen 3.6 27B	27B	S	11.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	33.3 tok/s
Qwen 3.5 35B A3B	35B	A	27.5 tok/s

Frequently asked questions

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

Yes, MacBook Pro M2 Max 32GB can run Mistral Small 24B with a A grade (Tight fit). Expected decode speed: 17.0 tok/s.

How much VRAM does Mistral Small 24B need?

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

What is the best quantization for Mistral Small 24B?

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

What speed will Mistral Small 24B run at on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, Mistral Small 24B 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 Mistral Small 24B for coding?

For coding workloads, Mistral Small 24B on MacBook Pro M2 Max 32GB receives a A grade with 17.0 tok/s and 27K context.

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

On MacBook Pro M2 Max 32GB, Mistral Small 24B can safely use up to 27K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 24B 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 Mistral Small 24B?

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 32GB See all hardware for Mistral Small 24B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/mistral-small-24b-on-m2-max-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: