Is unified memory on Mac mini M4 32GB as fast as VRAM for Mamba Codestral 7B v0.1?

Not always. Mac mini M4 32GB 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.

Can Mamba Codestral 7B v0.1 run on Mac mini M4 32GB?

YES — Runs Great

C46Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~9.4 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~23 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, 23.3 tok/s, Runs well

9.4 GB required23.0 GB available

41% VRAM used

Fit status

Runs well

Decode

23.3 tok/s

TTFT

8320 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 Mac mini M4 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: 23.3 tok/s decode · 8.3s TTFT (warm) · 58 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	23.3 tok/s	4538 ms	281K
Coding	C	Runs well	23.3 tok/s	8320 ms	281K
Agentic Coding	C	Runs well	23.3 tok/s	12102 ms	281K
Reasoning	C	Runs well	23.3 tok/s	9833 ms	281K
RAG	C	Runs well	23.3 tok/s	15127 ms	281K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on Mac mini M4 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

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

MacBook Pro M4 Pro 64GBBudget pick

64 GB Unified (+32)273 GB/s (+153)

Raises estimated decode speed by about 143%.56.6 tok/s decode

Raises estimated decode speed by about 143%.

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

~$1,599 MSRP

MacBook Pro M3 Pro 36GBBest value

36 GB Unified (+4)150 GB/s (+30)

Raises estimated decode speed by about 27%.29.5 tok/s decode

Raises estimated decode speed by about 27%.

~$1,999 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+32)800 GB/s (+680)

Raises estimated decode speed by about 321%.98 tok/s decode

Raises estimated decode speed by about 321%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 32GB run Mamba Codestral 7B v0.1?

Yes, Mac mini M4 32GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 23.3 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 9.4 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 M4 32GB?

On Mac mini M4 32GB, Mamba Codestral 7B v0.1 achieves approximately 23.3 tokens per second decode speed with a time-to-first-token of 8320ms using Q4_K_M quantization.

Can Mac mini M4 32GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on Mac mini M4 32GB receives a C grade with 23.3 tok/s and 281K context.

What context window can Mamba Codestral 7B v0.1 use on Mac mini M4 32GB?

On Mac mini M4 32GB, Mamba Codestral 7B v0.1 can safely use up to 281K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Mac mini M4 32GB See all hardware for Mamba Codestral 7B v0.1

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