Will It Run AI

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

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
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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

4K

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

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy2.5 tok/s42758 ms4K
CodingFToo heavy2.3 tok/s85865 ms4K
Agentic CodingFToo heavy2.0 tok/s140800 ms4K
ReasoningFToo heavy2.3 tok/s101477 ms4K
RAGFToo heavy2.0 tok/s176000 ms4K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
11.7 GB
LowA83
Q3_K_S
3
14.7 GB
LowF0
NVFP4
4
16.8 GB
MediumF0
Q4_K_M
4
18.3 GB
MediumF0
Q5_K_M
5
21.6 GB
HighF0
Q6_K
6
24.6 GB
HighF0
Q8_0
8
32.1 GB
Very HighF0
F16
16
61.5 GB
MaximumF0

Get started

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

Run

ollama run granite4.1:30b

Opções de upgrade

Hardware que roda bem Granite 4.1 30B

MacBook Pro M4 32GBMelhor custo-benefício
32 GB Unified (+8)120 GB/s (+20)
B
Makes the model fit on the accelerator instead of staying completely out of reach.7.3 tok/s decodificação

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 64GBOpção econômica
64 GB Unified (+40)120 GB/s (+20)
A
Makes the model fit on the accelerator instead of staying completely out of reach.9.1 tok/s decodificação

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 32GBUpgrade Apple
32 GB Unified (+8)120 GB/s (+20)
B
Makes the model fit on the accelerator instead of staying completely out of reach.7.3 tok/s decodificação

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

Raises estimated decode speed by about 217%.

~$1,099 MSRP

NVIDIANVIDIA A100 40GBMaior salto
40 GB VRAM (+16)1555 GB/s (+1455)
S
Makes the model fit on the accelerator instead of staying completely out of reach.76.7 tok/s decodificação

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 2.3 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.

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.

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.

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