Can Granite 3.1 8B run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

C54Usable

Estimated from fit model

Granite 3.1 8B needs ~11.2 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~33 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) — 11.2 GB, 35.5 tok/s, Runs well

11.2 GB required23.0 GB available

49% VRAM used

Fit status

Runs well

Decode

35.5 tok/s

TTFT

5459 ms

Safe context

113K

Memory

11.2 GB / 23.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on MacBook Pro M2 Pro 32GB
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: 35.5 tok/s decode · 5.5s TTFT (warm) · 89 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	35.5 tok/s	2977 ms	113K
Coding	C	Runs well	33.0 tok/s	5868 ms	113K
Agentic Coding	B	Runs well	35.5 tok/s	7940 ms	113K
Reasoning	C	Runs well	35.5 tok/s	6451 ms	113K
RAG	B	Runs well	35.5 tok/s	9925 ms	113K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C49
Q3_K_S	3	3.9 GB	Low	C50
NVFP4	4	4.5 GB	Medium	C50
Q4_K_M	4	4.9 GB	Medium	C50
Q5_K_M	5	5.8 GB	High	C51
Q6_K	6	6.6 GB	High	C51
Q8_0	8	8.6 GB	Very High	C53
F16Best for your GPU	16	16.4 GB	Maximum	C54

Get started

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

Run

ollama run granite3.1-dense

Opciones de mejora

Hardware que ejecuta bien Granite 3.1 8B

MacBook Pro M4 Max 36GBOpción económica

36 GB Unified (+4)410 GB/s (+210)

Sube la velocidad estimada de decodificación alrededor de un 84%.65.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 84%.

~$2,499 MSRP

MacBook Pro M4 Max 48GBMejor relación calidad-precio

48 GB Unified (+16)546 GB/s (+346)

Sube la velocidad estimada de decodificación alrededor de un 145%.87.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 145%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run Granite 3.1 8B?

Yes, MacBook Pro M2 Pro 32GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 33.0 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 11.2 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 MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Granite 3.1 8B achieves approximately 33.0 tokens per second decode speed with a time-to-first-token of 5868ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 32GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on MacBook Pro M2 Pro 32GB receives a C grade with 33.0 tok/s and 113K context.

What context window can Granite 3.1 8B use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Granite 3.1 8B can safely use up to 113K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for Granite 3.1 8B?

Not always. MacBook Pro M2 Pro 32GB 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 MacBook Pro M2 Pro 32GB See all hardware for Granite 3.1 8B

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