Can Codestral Mamba 7B run on Mac mini M2 24GB?

YES — Runs Great

A72Great

○Estimated from fit model

Codestral Mamba 7B needs ~8.3 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~18 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) — 8.3 GB, 17.5 tok/s, Runs well

8.3 GB required17.3 GB available

48% VRAM used

Fit status

Runs well

Decode

17.5 tok/s

TTFT

11059 ms

Safe context

262K

Memory

8.3 GB / 17.3 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on Mac mini M2 24GB
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: 17.5 tok/s decode · 11.1s TTFT (warm) · 44 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	17.5 tok/s	6032 ms	262K
Coding	A	Runs well	17.5 tok/s	11059 ms	262K
Agentic Coding	A	Runs well	17.5 tok/s	16086 ms	262K
Reasoning	A	Runs well	17.5 tok/s	13070 ms	262K
RAG	A	Runs well	17.5 tok/s	20108 ms	262K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A71
Q3_K_S	3	3.4 GB	Low	A72
NVFP4	4	3.9 GB	Medium	A72
Q4_K_M	4	4.3 GB	Medium	A73
Q5_K_M	5	5.0 GB	High	A73
Q6_K	6	5.7 GB	High	A74
Q8_0Best for your GPU	8	7.5 GB	Very High	A75
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral Mamba 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "mistralai/Mamba-Codestral-7B-v0.1" \
 --hf-file "Mamba-Codestral-7B-v0.1-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your Mac mini M2 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	12.7 tok/s
👁 Mistral Magistral Small 2507	24B	B	3.7 tok/s
👁 Mistral Devstral Small 2 24B Instruct	24B	B	3.7 tok/s
👁 Alibaba Qwen 3 14B	14B	S	8.2 tok/s
👁 Alibaba Qwen 3 8B	8B	S	14.3 tok/s

Frequently asked questions

See all results for Mac mini M2 24GB See all hardware for Codestral Mamba 7B

URL: https://willitrunai.com/can-run/codestral-mamba-7b-on-m2-24gb

⇱ Codestral Mamba 7B on Mac mini M2 24GB? YES