Is unified memory on Mac mini M2 24GB as fast as VRAM for Granite 4.1 30B?

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 Granite 4.1 30B run on Mac mini M2 24GB?

Q: What should I upgrade first if Granite 4.1 30B 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.

YES — With Q2_K

B67Good

Estimated from fit model

Granite 4.1 30B needs ~19.1 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q2_K 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.

Granite 4.1 30B at Q4_K_M needs 25.7 GB — too much for Mac mini M2 24GB (17.3 GB). Runs at Q2_K (19.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 25.7 GB, exceeds 17.3 GB available

25.7 GB required17.3 GB available

149% VRAM needed

8.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.3 tok/s

TTFT

85865 ms

Safe context

Memory

25.7 GB / 17.3 GB

Offload

30%

Memory breakdown

Weights18.3 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGranite 4.1 30B 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: 2.3 tok/s decode · 85.9s TTFT (warm) · 6 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 1.1 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	F	Too heavy	2.5 tok/s	42758 ms	4K
Coding	F	Too heavy	2.1 tok/s	92305 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.3 tok/s	101477 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	11.7 GB	Low	A83
Q3_K_S	3	14.7 GB	Low	F0

Get started

Copy-paste commands to run Granite 4.1 30B on your machine.

Run

ollama run granite4.1:30b

Upgrade options

Hardware that runs Granite 4.1 30B well

MacBook Pro M4 32GBBest value

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

Makes the model fit on the accelerator instead of staying completely out of reach.7.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 217%.

~$799 MSRP

Mac mini M4 64GBBudget pick

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

Makes the model fit on the accelerator instead of staying completely out of reach.9.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,099 MSRP

Mac mini M4 32GBApple upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.7.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 217%.

~$1,099 MSRP

NVIDIA A100 40GBBiggest leap

40 GB VRAM (+16)1555 GB/s (+1455)

Makes the model fit on the accelerator instead of staying completely out of reach.76.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$10,000 MSRP

Frequently asked questions

Can Mac mini M2 24GB run Granite 4.1 30B?

Yes, Mac mini M2 24GB can run Granite 4.1 30B at Q2_K quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q4_K_M requires 25.7 GB which exceeds available memory, but at Q2_K it needs only 19.1 GB. Expected decode speed: 4.3 tok/s.

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 25.7 GB at Q4_K_M quantization. On Mac mini M2 24GB, it fits at Q2_K using 19.1 GB.

What is the best quantization for Granite 4.1 30B?

The recommended quantization is Q4_K_M, but on Mac mini M2 24GB the best fitting quantization is Q2_K, which uses 19.1 GB.

What speed will Granite 4.1 30B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Granite 4.1 30B achieves approximately 4.3 tokens per second decode speed with a time-to-first-token of 44993ms using Q2_K quantization.

Can Mac mini M2 24GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on Mac mini M2 24GB receives a F grade with 2.1 tok/s and 4K context.

What context window can Granite 4.1 30B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Granite 4.1 30B can safely use up to 9K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Mac mini M2 24GB See all hardware for Granite 4.1 30B

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