Can Devstral Small 2 24B Instruct run on MacBook Pro M1 Max 64GB?

YES — Runs Great

S89Excellent

○Estimated from fit model

Devstral Small 2 24B Instruct needs ~24.9 GB VRAM. MacBook Pro M1 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 24.9 GB, 16.2 tok/s, Runs well

24.9 GB required46.1 GB available

54% VRAM used

Fit status

Runs well

Decode

16.2 tok/s

TTFT

11984 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 MacBook Pro M1 Max 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: 16.2 tok/s decode · 12.0s TTFT (warm) · 40 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	S	Runs well	16.2 tok/s	6537 ms	155K
Coding	S	Runs well	16.2 tok/s	11984 ms	155K
Agentic Coding	S	Runs well	16.2 tok/s	17432 ms	155K
Reasoning	S	Runs well	16.2 tok/s	14163 ms	155K
RAG	S	Runs well	16.2 tok/s	21790 ms	155K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on MacBook Pro M1 Max 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 MacBook Pro M1 Max 64GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	33.3 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	14.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	11 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	30.8 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	34.4 tok/s

Frequently asked questions

See all results for MacBook Pro M1 Max 64GB See all hardware for Devstral Small 2 24B Instruct

URL: https://willitrunai.com/can-run/devstral-small-2-24b-on-m1-max-64gb

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