Can Mamba Codestral 7B v0.1 run on Mac mini M2 24GB?

YES — Runs Great

C47Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~8.6 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~18 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) — 8.6 GB, 17.5 tok/s, Runs well

8.6 GB required17.3 GB available

50% VRAM used

Fit status

Runs well

Decode

17.5 tok/s

TTFT

11059 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 Mac mini M2 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: 17.5 tok/s decode · 11.1s TTFT (warm) · 44 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	17.5 tok/s	6032 ms	186K
Coding	C	Runs well	17.5 tok/s	11059 ms	186K
Agentic Coding	C	Runs well	17.5 tok/s	16086 ms	186K
Reasoning	C	Runs well	17.5 tok/s	13070 ms	186K
RAG	C	Runs well	17.5 tok/s	20108 ms	186K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on Mac mini M2 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

Opciones de mejora

Hardware que ejecuta bien Mamba Codestral 7B v0.1

MacBook Pro M2 Max 32GBOpción económica

32 GB Unified (+8)400 GB/s (+300)

Sube la velocidad estimada de decodificación alrededor de un 257%.62.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 257%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

MacBook Pro M2 Pro 32GBMejor relación calidad-precio

32 GB Unified (+8)200 GB/s (+100)

Sube la velocidad estimada de decodificación alrededor de un 115%.37.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 115%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

Mac Studio M2 Ultra 64GBMejora Apple

64 GB Unified (+40)800 GB/s (+700)

Sube la velocidad estimada de decodificación alrededor de un 460%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 460%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run Mamba Codestral 7B v0.1?

Yes, Mac mini M2 24GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 17.5 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 8.6 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 Mac mini M2 24GB?

On Mac mini M2 24GB, Mamba Codestral 7B v0.1 achieves approximately 17.5 tokens per second decode speed with a time-to-first-token of 11059ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on Mac mini M2 24GB receives a C grade with 17.5 tok/s and 186K context.

What context window can Mamba Codestral 7B v0.1 use on Mac mini M2 24GB?

On Mac mini M2 24GB, Mamba Codestral 7B v0.1 can safely use up to 186K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for Mamba Codestral 7B v0.1?

Not always. Mac mini M2 24GB 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 Mac mini M2 24GB See all hardware for Mamba Codestral 7B v0.1

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