Is unified memory on Mac mini M4 64GB as fast as VRAM for Codestral 22B v0.1?

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.

Can Codestral 22B v0.1 run on Mac mini M4 64GB?

Q: What should I upgrade first if Codestral 22B v0.1 feels slow on Mac mini M4 64GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Runs Great

C44Usable

Estimated — low-sample bucket· few comparable runs

Codestral 22B v0.1 needs ~23.8 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~6 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) — 23.8 GB, 9.0 tok/s, Runs well

23.8 GB required46.1 GB available

52% VRAM used

Fit status

Runs well

Decode

9.0 tok/s

TTFT

21479 ms

Safe context

154K

Memory

23.8 GB / 46.1 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 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: 9.0 tok/s decode · 21.5s TTFT (warm) · 23 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	9.0 tok/s	11716 ms	154K
Coding	C	Runs well	6.4 tok/s	30071 ms	154K
Agentic Coding	C	Runs well	9.0 tok/s	31242 ms	154K
Reasoning	C	Runs well	9.0 tok/s	25384 ms	154K
RAG	C	Runs well	6.4 tok/s	54674 ms	154K

Quantization options

How Codestral 22B v0.1 (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	C43
Q3_K_S	3	10.8 GB	Low	C43
NVFP4	4

Get started

Copy-paste commands to run Codestral 22B v0.1 on your machine.

Run

lms load hf-sanctumai--codestral-22b-v0-1-gguf && lms server start

Upgrade options

Hardware that runs Codestral 22B v0.1 well

MacBook Pro M4 Max 96GBBudget pick

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

Raises estimated decode speed by about 287%.34.8 tok/s decode

Raises estimated decode speed by about 287%.

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

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

Raises estimated decode speed by about 361%.41.5 tok/s decode

Raises estimated decode speed by about 361%.

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

~$3,999 MSRP

Mac Studio M2 Ultra 128GBApple upgrade

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

Raises estimated decode speed by about 284%.34.6 tok/s decode

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 v0.1?

Yes, Mac mini M4 64GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 6.4 tok/s.

How much VRAM does Codestral 22B v0.1 need?

Codestral 22B v0.1 (22B parameters) requires approximately 23.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1?

The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 run at on Mac mini M4 64GB?

On Mac mini M4 64GB, Codestral 22B v0.1 achieves approximately 6.4 tokens per second decode speed with a time-to-first-token of 30071ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on Mac mini M4 64GB receives a C grade with 6.4 tok/s and 154K context.

What context window can Codestral 22B v0.1 use on Mac mini M4 64GB?

On Mac mini M4 64GB, Codestral 22B v0.1 can safely use up to 154K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 22B v0.1 feels slow on Mac mini M4 64GB?

See all results for Mac mini M4 64GB See all hardware for Codestral 22B v0.1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-sanctumai--codestral-22b-v0-1-gguf-on-m4-mini-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: