Is unified memory on MacBook Pro M4 32GB as fast as VRAM for Devstral Small 2 24B Instruct?

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 Devstral Small 2 24B Instruct run on MacBook Pro M4 32GB?

Q: What should I upgrade first if Devstral Small 2 24B Instruct feels slow on MacBook Pro M4 32GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Tight Fit

S87Excellent

Estimated — low-sample bucket· few comparable runs

Devstral Small 2 24B Instruct needs ~21.4 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~6 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 feelsDevstral Small 2 24B Instruct 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

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

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	S	Tight fit	5.9 tok/s	17893 ms	27K
Coding	S	Tight fit	5.9 tok/s	32804 ms	27K
Agentic Coding	S	Runs with offload	5.5 tok/s	51232 ms	27K
Reasoning	S	Tight fit	5.9 tok/s	38769 ms	27K
RAG	S	Runs with offload	5.5 tok/s	64040 ms	27K

Quantization options

How Devstral Small 2 24B Instruct (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	S90
Q3_K_S	3	11.8 GB	Low	S92
NVFP4	4

Get started

Copy-paste commands to run Devstral Small 2 24B Instruct on your machine.

Run

ollama run devstral-small-2

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 Devstral Small 2 24B Instruct?

Yes, MacBook Pro M4 32GB can run Devstral Small 2 24B Instruct with a S grade (Tight fit). Expected decode speed: 5.9 tok/s.

How much VRAM does Devstral Small 2 24B Instruct need?

Devstral Small 2 24B Instruct (24B parameters) requires approximately 21.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral Small 2 24B Instruct?

The recommended quantization for Devstral Small 2 24B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral Small 2 24B Instruct run at on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Devstral Small 2 24B Instruct achieves approximately 5.9 tokens per second decode speed with a time-to-first-token of 32804ms using Q4_K_M quantization.

Can MacBook Pro M4 32GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on MacBook Pro M4 32GB receives a S grade with 5.9 tok/s and 27K context.

What context window can Devstral Small 2 24B Instruct use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Devstral Small 2 24B Instruct can safely use up to 27K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 32GB See all hardware for Devstral Small 2 24B Instruct

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