Can Mamba Codestral 7B v0.1 run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

C47Usable

○Estimated from fit model

Mamba Codestral 7B v0.1 needs ~9.4 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~38 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) — 9.4 GB, 37.7 tok/s, Runs well

9.4 GB required23.0 GB available

41% VRAM used

Fit status

Runs well

Decode

37.7 tok/s

TTFT

5135 ms

Safe context

281K

Memory

9.4 GB / 23.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on MacBook Pro M2 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: 37.7 tok/s decode · 5.1s TTFT (warm) · 94 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	37.7 tok/s	2801 ms	281K
Coding	C	Runs well	37.7 tok/s	5135 ms	281K
Agentic Coding	C	Runs well	37.7 tok/s	7469 ms	281K
Reasoning	C	Runs well	37.7 tok/s	6068 ms	281K
RAG	C	Runs well	37.7 tok/s	9336 ms	281K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C44
Q3_K_S	3	3.4 GB	Low	C44
NVFP4	4	3.9 GB	Medium	C45
Q4_K_M	4	4.3 GB	Medium	C45
Q5_K_M	5	5.0 GB	High	C45
Q6_K	6	5.7 GB	High	C46
Q8_0	8	7.5 GB	Very High	C47
F16Best for your GPU	16	14.3 GB	Maximum	C50