Can Mistral Small 24B run on Mac mini M4 64GB?

YES — Runs Great

A78Great

▲Estimated — low-sample bucket· few comparable runs

Mistral Small 24B needs ~24.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~10 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

33K

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 feelsMistral Small 24B 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

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	9.5 tok/s	11097 ms	33K
Coding	A	Runs well	9.5 tok/s	20344 ms	33K
Agentic Coding	A	Runs well	9.5 tok/s	29591 ms	33K
Reasoning	A	Runs well	9.5 tok/s	24043 ms	33K
RAG	A	Runs well	9.5 tok/s	36989 ms	33K

Quantization options

How Mistral Small 24B (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	A75
Q3_K_S	3	11.8 GB	Low	A76
NVFP4	4	13.4 GB	Medium	A76
Q4_K_M	4	14.6 GB	Medium	A77
Q5_K_M	5	17.3 GB	High	A78
Q6_K	6	19.7 GB	High	A78
Q8_0Best for your GPU	8	25.7 GB	Very High	A80
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 Mac mini M4 64GB can run

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

Frequently asked questions

See all results for Mac mini M4 64GB See all hardware for Mistral Small 24B

URL: https://willitrunai.com/can-run/mistral-small-24b-on-m4-mini-64gb

⇱ Mistral Small 24B on Mac mini M4 64GB? YES