Can Mamba Codestral 7B v0.1 run on MacBook Pro M2 Max 96GB?

YES — Runs Great

C45Usable

○Estimated from fit model

Mamba Codestral 7B v0.1 needs ~16.4 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~63 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) — 16.4 GB, 62.5 tok/s, Runs well

16.4 GB required69.1 GB available

24% VRAM used

Fit status

Runs well

Decode

62.5 tok/s

TTFT

3098 ms

Safe context

1.0M

Memory

16.4 GB / 69.1 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on MacBook Pro M2 Max 96GB
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: 62.5 tok/s decode · 3.1s TTFT (warm) · 156 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	62.5 tok/s	1690 ms	1.0M
Coding	C	Runs well	62.5 tok/s	3098 ms	1.0M
Agentic Coding	C	Runs well	62.5 tok/s	4507 ms	1.0M
Reasoning	C	Runs well	62.5 tok/s	3662 ms	1.0M
RAG	C	Runs well	62.5 tok/s	5634 ms	1.0M

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D40
Q3_K_S	3	3.4 GB	Low	D40
NVFP4	4	3.9 GB	Medium	D40
Q4_K_M	4	4.3 GB	Medium	D40
Q5_K_M	5	5.0 GB	High	D40
Q6_K	6	5.7 GB	High	D40
Q8_0	8	7.5 GB	Very High	C40
F16Best for your GPU	16	14.3 GB	Maximum	C41