Is unified memory on Mac mini M2 24GB as fast as VRAM for Codestral 22B 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.

Can Codestral 22B v0.1 run on Mac mini M2 24GB?

Q: What should I upgrade first if Codestral 22B v0.1 feels slow on Mac mini M2 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

D34Poor

Estimated from fit model

Codestral 22B v0.1 needs ~19.5 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~4 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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) — 19.5 GB, 4.0 tok/s, Very compromised (needs ~1.5 GB host RAM)

19.5 GB required17.3 GB available

113% VRAM needed

2.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.5 GB host RAM)

Decode

4.0 tok/s

TTFT

48465 ms

Safe context

Memory

19.5 GB / 17.3 GB

Offload

10%

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsCodestral 22B 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: 4.0 tok/s decode · 48.5s TTFT (warm) · 10 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	D	Runs with offload (needs ~0.7 GB host RAM)	4.4 tok/s	24009 ms	4K
Coding	D	Very compromised (needs ~1.5 GB host RAM)	4.0 tok/s	48465 ms	4K
Agentic Coding	F	Too heavy	3.4 tok/s	82412 ms	4K
Reasoning	D	Very compromised (needs ~1.5 GB host RAM)	4.0 tok/s	57277 ms	4K
RAG	F	Too heavy	3.4 tok/s	103015 ms

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C51
Q3_K_S	3	10.8 GB	Low	C51
NVFP4Best for your GPU

Get started

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

Run

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

Upgrade options

Hardware that runs Codestral 22B v0.1 well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+8)120 GB/s (+20)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 125%.

~$799 MSRP

Mac mini M4 32GBBest value

32 GB Unified (+8)120 GB/s (+20)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 125%.

~$1,099 MSRP

Mac mini M4 64GBApple upgrade

64 GB Unified (+40)120 GB/s (+20)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 125%.

~$1,099 MSRP

RTX 5090 Laptop 24GBBiggest leap

896 GB/s (+796)

Removes host-memory offload, which is usually the single biggest latency and throughput win.56.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 1303%.

Frequently asked questions

Can Mac mini M2 24GB run Codestral 22B v0.1?

Yes, Mac mini M2 24GB can run Codestral 22B v0.1 with a D grade (Very compromised (needs ~1.5 GB host RAM)). Expected decode speed: 4.0 tok/s.

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

Codestral 22B v0.1 (22B parameters) requires approximately 19.5 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 M2 24GB?

On Mac mini M2 24GB, Codestral 22B v0.1 achieves approximately 4.0 tokens per second decode speed with a time-to-first-token of 48465ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on Mac mini M2 24GB receives a D grade with 4.0 tok/s and 4K context.

What context window can Codestral 22B v0.1 use on Mac mini M2 24GB?

On Mac mini M2 24GB, Codestral 22B v0.1 can safely use up to 4K 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 M2 24GB?

See all results for Mac mini M2 24GB See all hardware for Codestral 22B v0.1

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