Can Granite 3.1 8B run on Mac mini M2 24GB?

YES — Runs Great

C54Usable

Estimated from fit model

Granite 3.1 8B needs ~10.3 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~17 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) — 10.3 GB, 16.5 tok/s, Runs well

10.3 GB required17.3 GB available

60% VRAM used

Fit status

Runs well

Decode

16.5 tok/s

TTFT

11757 ms

Safe context

73K

Memory

10.3 GB / 17.3 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B 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: 16.5 tok/s decode · 11.8s TTFT (warm) · 41 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	C	Runs well	16.5 tok/s	6413 ms	73K
Coding	C	Runs well	16.5 tok/s	11757 ms	73K
Agentic Coding	B	Runs well	16.5 tok/s	17101 ms	73K
Reasoning	C	Runs well	16.5 tok/s	13895 ms	73K
RAG	B	Runs well	16.5 tok/s	21377 ms	73K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C51
Q3_K_S	3	3.9 GB	Low	C52
NVFP4	4	4.5 GB	Medium	C52
Q4_K_M	4	4.9 GB	Medium	C53
Q5_K_M	5	5.8 GB	High	C53
Q6_K	6	6.6 GB	High	C54
Q8_0Best for your GPU	8	8.6 GB	Very High	B56
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Granite 3.1 8B on your machine.

Run

ollama run granite3.1-dense

Opções de upgrade

Hardware que roda bem Granite 3.1 8B

MacBook Pro M2 Max 32GBOpção econômica

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 256%.58.8 tok/s decodificação

Raises estimated decode speed by about 256%.

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

~$1,999 MSRP

MacBook Pro M2 Pro 32GBMelhor custo-benefício

32 GB Unified (+8)200 GB/s (+100)

Raises estimated decode speed by about 115%.35.5 tok/s decodificação

Raises estimated decode speed by about 115%.

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

~$1,999 MSRP

MacBook Pro M1 Max 32GBUpgrade Apple

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 238%.55.7 tok/s decodificação

Raises estimated decode speed by about 238%.

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

~$2,499 MSRP

Frequently asked questions

Can Mac mini M2 24GB run Granite 3.1 8B?

Yes, Mac mini M2 24GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 16.5 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 3.1 8B?

The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 3.1 8B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Granite 3.1 8B achieves approximately 16.5 tokens per second decode speed with a time-to-first-token of 11757ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on Mac mini M2 24GB receives a C grade with 16.5 tok/s and 73K context.

What context window can Granite 3.1 8B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Granite 3.1 8B can safely use up to 73K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for Granite 3.1 8B?

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 24GB See all hardware for Granite 3.1 8B

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