Can Codestral 22B run on Mac mini M4 64GB?

YES — Runs Great

B56Good

Estimated — low-sample bucket· few comparable runs

Codestral 22B needs ~23.7 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~10 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) — 23.7 GB, 9.7 tok/s, Runs well

23.7 GB required46.1 GB available

51% VRAM used

Fit status

Runs well

Decode

9.7 tok/s

TTFT

19981 ms

Safe context

33K

Memory

23.7 GB / 46.1 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsCodestral 22B on Mac mini M4 64GB
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: 9.7 tok/s decode · 20.0s TTFT (warm) · 24 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	B	Runs well	9.7 tok/s	10898 ms	33K
Coding	B	Runs well	9.7 tok/s	19981 ms	33K
Agentic Coding	B	Runs well	9.7 tok/s	29063 ms	33K
Reasoning	B	Runs well	9.7 tok/s	23613 ms	33K
RAG	B	Runs well	9.7 tok/s	36328 ms	33K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C53
Q3_K_S	3	10.8 GB	Low	C53
NVFP4	4	12.3 GB	Medium	C54
Q4_K_M	4	13.4 GB	Medium	C54
Q5_K_M	5	15.8 GB	High	B55
Q6_K	6	18.0 GB	High	B56
Q8_0Best for your GPU	8	23.5 GB	Very High	B58
F16	16	45.1 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral 22B on your machine.

Run

ollama run codestral

升级选项

能流畅运行 Codestral 22B 的硬件

MacBook Pro M4 Max 96GB经济之选

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 286%.37.4 tok/s 解码

Raises estimated decode speed by about 286%.

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GB最佳性价比

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 360%.44.6 tok/s 解码

Raises estimated decode speed by about 360%.

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

~$3,999 MSRP

Mac Studio M2 Ultra 128GBApple 升级

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

Raises estimated decode speed by about 284%.37.2 tok/s 解码

Raises estimated decode speed by about 284%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Codestral 22B?

Yes, Mac mini M4 64GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 9.7 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 23.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B?

The recommended quantization for Codestral 22B is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B run at on Mac mini M4 64GB?

On Mac mini M4 64GB, Codestral 22B achieves approximately 9.7 tokens per second decode speed with a time-to-first-token of 19981ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on Mac mini M4 64GB receives a B grade with 9.7 tok/s and 33K context.

What context window can Codestral 22B use on Mac mini M4 64GB?

On Mac mini M4 64GB, Codestral 22B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Codestral 22B?

Not always. Mac mini M4 64GB 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 M4 64GB See all hardware for Codestral 22B

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