Can Mamba Codestral 7B v0.1 run on MacBook Pro M4 Pro 48GB?

YES — Runs Great

C47Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~11.2 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 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) — 11.2 GB, 56.6 tok/s, Runs well

11.2 GB required34.6 GB available

32% VRAM used

Fit status

Runs well

Decode

56.6 tok/s

TTFT

3420 ms

Safe context

472K

Memory

11.2 GB / 34.6 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on MacBook Pro M4 Pro 48GB
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	472K
Coding	C	Runs well	56.6 tok/s	3420 ms	472K
Agentic Coding	C	Runs well	56.6 tok/s	4974 ms	472K
Reasoning	C	Runs well	56.6 tok/s	4041 ms	472K
RAG	C	Runs well	56.6 tok/s	6218 ms	472K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C42
Q3_K_S	3	3.4 GB	Low	C42
NVFP4	4	3.9 GB	Medium	C43
Q4_K_M	4	4.3 GB	Medium	C43
Q5_K_M	5	5.0 GB	High	C43
Q6_K	6	5.7 GB	High	C43
Q8_0	8	7.5 GB	Very High	C44
F16Best for your GPU	16	14.3 GB	Maximum	C47

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

升级选项

能流畅运行 Mamba Codestral 7B v0.1 的硬件

Mac Studio M2 Ultra 64GB经济之选

64 GB Unified (+16)800 GB/s (+527)

Raises estimated decode speed by about 73%.98 tok/s 解码

Raises estimated decode speed by about 73%.

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

~$3,999 MSRP

MacBook Pro M4 Max 64GB最佳性价比

64 GB Unified (+16)546 GB/s (+273)

Raises estimated decode speed by about 64%.92.6 tok/s 解码

Raises estimated decode speed by about 64%.

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

~$3,999 MSRP

Mac Studio M1 Ultra 64GBApple 升级

64 GB Unified (+16)800 GB/s (+527)

Raises estimated decode speed by about 73%.98 tok/s 解码

Raises estimated decode speed by about 73%.

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

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run Mamba Codestral 7B v0.1?

Yes, MacBook Pro M4 Pro 48GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 56.6 tok/s.

How much VRAM does Mamba Codestral 7B v0.1 need?

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 11.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Mamba Codestral 7B v0.1?

The recommended quantization for Mamba Codestral 7B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mamba Codestral 7B v0.1 run at on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, Mamba Codestral 7B v0.1 achieves approximately 56.6 tokens per second decode speed with a time-to-first-token of 3420ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on MacBook Pro M4 Pro 48GB receives a C grade with 56.6 tok/s and 472K context.

What context window can Mamba Codestral 7B v0.1 use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, Mamba Codestral 7B v0.1 can safely use up to 472K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for Mamba Codestral 7B v0.1?

Not always. MacBook Pro M4 Pro 48GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for MacBook Pro M4 Pro 48GB See all hardware for Mamba Codestral 7B v0.1

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