Can Codestral 2 25.08 run on MacBook Pro M1 Pro 32GB?

YES — Tight Fit

A81Great

○Estimated from fit model

Codestral 2 25.08 needs ~20.2 GB VRAM. MacBook Pro M1 Pro 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) — 20.2 GB, 9.8 tok/s, Tight fit

20.2 GB required23.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

9.8 tok/s

TTFT

19778 ms

Safe context

34K

Memory

20.2 GB / 23.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsCodestral 2 25.08 on MacBook Pro M1 Pro 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: 9.8 tok/s decode · 19.8s TTFT (warm) · 25 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	Tight fit	9.8 tok/s	10788 ms	34K
Coding	A	Tight fit	9.8 tok/s	19778 ms	34K
Agentic Coding	A	Runs with offload	9.8 tok/s	28768 ms	34K
Reasoning	A	Tight fit	9.8 tok/s	23374 ms	34K
RAG	A	Runs with offload	9.8 tok/s	35960 ms	34K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	A83
Q3_K_S	3	10.8 GB	Low	A84
NVFP4	4	12.3 GB	Medium	A85
Q4_K_M	4	13.4 GB	Medium	A84
Q5_K_M	5	15.8 GB	High	A84
Q6_KBest for your GPU	6	18.0 GB	High	A84
Q8_0	8	23.5 GB	Very High	F0
F16	16	45.1 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral 2 25.08 on your machine.

Run

lms load codestral-2508 && lms server start

Your hardware

More models your MacBook Pro M1 Pro 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	A	17.7 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	7.9 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	6.5 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	18.6 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	A	15.4 tok/s

Frequently asked questions

See all results for MacBook Pro M1 Pro 32GB See all hardware for Codestral 2 25.08

URL: https://willitrunai.com/can-run/codestral-2-25.08-on-m1-pro-32gb

⇱ Codestral 2 25.08 on MacBook Pro M1 Pro 32GB? TIGHT FIT