Can Devstral Small 2 24B Instruct run on Mac mini M4 64GB?

YES — Runs Great

S86Excellent

Estimated — low-sample bucket· few comparable runs

Devstral Small 2 24B Instruct needs ~24.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 24.9 GB, 9.5 tok/s, Runs well

24.9 GB required46.1 GB available

54% VRAM used

Fit status

Runs well

Decode

9.5 tok/s

TTFT

20344 ms

Safe context

155K

Memory

24.9 GB / 46.1 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsDevstral Small 2 24B Instruct on Mac mini M4 64GB
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.

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	5.9 tok/s	17893 ms	155K
Coding	S	Runs well	5.9 tok/s	32804 ms	155K
Agentic Coding	S	Runs well	5.9 tok/s	47716 ms	155K
Reasoning	S	Runs well	5.9 tok/s	38769 ms	155K
RAG	S	Runs well	5.9 tok/s	59644 ms	155K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A84
Q3_K_S	3	11.8 GB	Low	S85
NVFP4	4	13.4 GB	Medium	S86
Q4_K_M	4	14.6 GB	Medium	S86
Q5_K_M	5	17.3 GB	High	S87
Q6_K	6	19.7 GB	High	S88
Q8_0Best for your GPU	8	25.7 GB	Very High	S90
F16	16	49.2 GB	Maximum	F0

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 Mac mini M4 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	13.1 tok/s
Qwen 3.5 27B	27B	S	9.3 tok/s
Qwen 3.6 27B	27B	S	7.1 tok/s
Qwen 3.6 35B A3B	35B	S	12.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	13.5 tok/s

Frequently asked questions

Can Mac mini M4 64GB run Devstral Small 2 24B Instruct?

Yes, Mac mini M4 64GB can run Devstral Small 2 24B Instruct with a S grade (Runs well). 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 24.9 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 Mac mini M4 64GB?

On Mac mini M4 64GB, 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 Mac mini M4 64GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on Mac mini M4 64GB receives a S grade with 5.9 tok/s and 155K context.

What context window can Devstral Small 2 24B Instruct use on Mac mini M4 64GB?

On Mac mini M4 64GB, Devstral Small 2 24B Instruct can safely use up to 155K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Devstral Small 2 24B Instruct feels slow on Mac mini M4 64GB?

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.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Devstral Small 2 24B Instruct?

Not always. Mac mini M4 64GB 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 Mac mini M4 64GB See all hardware for Devstral Small 2 24B Instruct

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