Can Mamba Codestral 7B v0.1 run on MacBook Pro M4 Pro 24GB?

YES — Runs Great

C51Usable

○Estimated from fit model

Mamba Codestral 7B v0.1 needs ~8.6 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~57 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) — 8.6 GB, 56.6 tok/s, Runs well

8.6 GB required17.3 GB available

50% VRAM used

Fit status

Runs well

Decode

56.6 tok/s

TTFT

3420 ms

Safe context

186K

Memory

8.6 GB / 17.3 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on MacBook Pro M4 Pro 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: 56.6 tok/s decode · 3.4s TTFT (warm) · 142 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	C	Runs well	56.6 tok/s	1865 ms	186K
Coding	C	Runs well	56.6 tok/s	3420 ms	186K
Agentic Coding	C	Runs well	56.6 tok/s	4974 ms	186K
Reasoning	C	Runs well	56.6 tok/s	4041 ms	186K
RAG	C	Runs well	56.6 tok/s	6218 ms	186K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C46
Q3_K_S	3	3.4 GB	Low	C46
NVFP4	4	3.9 GB	Medium	C47
Q4_K_M	4	4.3 GB	Medium	C47
Q5_K_M	5	5.0 GB	High	C48
Q6_K	6	5.7 GB	High	C48
Q8_0Best for your GPU	8	7.5 GB	Very High	C50
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run Mamba Codestral 7B v0.1 on your machine.

Run

lms load hf-gabriellarson--mamba-codestral-7b-v0-1-gguf && lms server start

Frequently asked questions

See all results for MacBook Pro M4 Pro 24GB See all hardware for Mamba Codestral 7B v0.1

URL: https://willitrunai.com/can-run/hf-gabriellarson--mamba-codestral-7b-v0-1-gguf-on-m4-pro-24gb

⇱ Mamba Codestral 7B v0.1 on MacBook Pro M4 Pro 24GB? YES