Is unified memory on MacBook Pro M4 32GB as fast as VRAM for Mistral Small 3.1 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.

Can Mistral Small 3.1 24B run on MacBook Pro M4 32GB?

Q: What should I upgrade first if Mistral Small 3.1 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.

YES — Tight Fit

A78Great

Estimated — low-sample bucket· few comparable runs

Mistral Small 3.1 24B needs ~21.4 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: 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) — 21.4 GB, 9.5 tok/s, Tight fit

21.4 GB required23.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

9.5 tok/s

TTFT

20344 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 3.1 24B on MacBook Pro M4 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: 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	9.5 tok/s	11097 ms	27K
Coding	A	Tight fit	9.5 tok/s	20344 ms	27K
Agentic Coding	A	Runs with offload (needs ~0.5 GB host RAM)	8.9 tok/s	31772 ms	27K
Reasoning	A	Tight fit	9.5 tok/s	24043 ms	27K
RAG	A	Runs with offload (needs ~0.5 GB host RAM)	8.9 tok/s	39714 ms

Quantization options

How Mistral Small 3.1 24B (24B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A80
Q3_K_S	3	11.8 GB	Low	A82
NVFP4	4

Get started

Copy-paste commands to run Mistral Small 3.1 24B on your machine.

Run

ollama run mistral-small:24b

Your hardware

More models your MacBook Pro M4 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	11.7 tok/s
Qwen 3.5 27B	27B	S	8.6 tok/s

Frequently asked questions

Can MacBook Pro M4 32GB run Mistral Small 3.1 24B?

Yes, MacBook Pro M4 32GB can run Mistral Small 3.1 24B with a A grade (Tight fit). Expected decode speed: 9.5 tok/s.

How much VRAM does Mistral Small 3.1 24B need?

Mistral Small 3.1 24B (24B parameters) requires approximately 21.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.1 24B?

The recommended quantization for Mistral Small 3.1 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.1 24B run at on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Mistral Small 3.1 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 3.1 24B for coding?

For coding workloads, Mistral Small 3.1 24B on MacBook Pro M4 32GB receives a A grade with 9.5 tok/s and 27K context.

What context window can Mistral Small 3.1 24B use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Mistral Small 3.1 24B can safely use up to 27K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 32GB See all hardware for Mistral Small 3.1 24B

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