Can Mamba Codestral 7B v0.1 run on MacBook Pro M3 Pro 18GB?

YES — Runs Great

C51Usable

○Estimated from fit model

Mamba Codestral 7B v0.1 needs ~7.9 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~30 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) — 7.9 GB, 29.5 tok/s, Runs well

7.9 GB required13.0 GB available

61% VRAM used

Fit status

Runs well

Decode

29.5 tok/s

TTFT

6565 ms

Safe context

114K

Memory

7.9 GB / 13.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom1.9 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on MacBook Pro M3 Pro 18GB
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: 29.5 tok/s decode · 6.6s TTFT (warm) · 74 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	29.5 tok/s	3581 ms	114K
Coding	C	Runs well	29.5 tok/s	6565 ms	114K
Agentic Coding	C	Runs well	29.5 tok/s	9549 ms	114K
Reasoning	C	Runs well	29.5 tok/s	7758 ms	114K
RAG	C	Runs well	29.5 tok/s	11936 ms	114K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C48
Q3_K_S	3	3.4 GB	Low	C49
NVFP4	4	3.9 GB	Medium	C49
Q4_K_M	4	4.3 GB	Medium	C50
Q5_K_M	5	5.0 GB	High	C51
Q6_K	6	5.7 GB	High	C52
Q8_0Best for your GPU	8	7.5 GB	Very High	C51
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

Upgrade options

Hardware that runs Mamba Codestral 7B v0.1 well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick

448 GB/s (+298)

Raises estimated decode speed by about 154%.74.8 tok/s decode

Raises estimated decode speed by about 154%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RX 9070 16GBBest value

640 GB/s (+490)

Raises estimated decode speed by about 232%.98 tok/s decode

Raises estimated decode speed by about 232%.