Can Codestral 2 25.08 run on MacBook Pro M2 Pro 16GB?

YES — With Q2_K

B65Good

○Estimated from fit model

Codestral 2 25.08 needs ~13.6 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q2_K quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

Codestral 2 25.08 at Q4_K_M needs 18.5 GB — too much for MacBook Pro M2 Pro 16GB (11.5 GB). Runs at Q2_K (13.6 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 18.5 GB, exceeds 11.5 GB available

18.5 GB required11.5 GB available

161% VRAM needed

7.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.7 tok/s

TTFT

33894 ms

Safe context

Memory

18.5 GB / 11.5 GB

Offload

40%

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral 2 25.08 on MacBook Pro M2 Pro 16GB
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: 5.7 tok/s decode · 33.9s TTFT (warm) · 14 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	6.2 tok/s	17118 ms	4K
Coding	F	Too heavy	5.7 tok/s	33894 ms	4K
Agentic Coding	F	Too heavy	5.0 tok/s	56543 ms	4K
Reasoning	F	Too heavy	5.7 tok/s	40057 ms	4K
RAG	F	Too heavy	5.0 tok/s	70678 ms	4K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	F0
Q3_K_S	3	10.8 GB	Low	F0
NVFP4	4

Get started

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

Run

lms load codestral-2508 && lms server start

Upgrade options

Hardware that runs Codestral 2 25.08 well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.9.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$799 MSRP

Mac mini M4 32GBBest value

32 GB Unified (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.9.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

Mac mini M4 64GBApple upgrade

64 GB Unified (+48)

Makes the model fit on the accelerator instead of staying completely out of reach.9.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

👁 NVIDIA
RTX 5090 Laptop 24GBBiggest leap

24 GB VRAM (+8)896 GB/s (+696)

Makes the model fit on the accelerator instead of staying completely out of reach.53.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Frequently asked questions

See all results for MacBook Pro M2 Pro 16GB See all hardware for Codestral 2 25.08

URL: https://willitrunai.com/can-run/codestral-2-25.08-on-m2-pro-16gb

⇱

Can Codestral 2 25.08 run on MacBook Pro M2 Pro 16GB?

YES — With Q2_K

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Codestral 2 25.08 well

Frequently asked questions