Can Granite 3.1 8B run on MacBook Pro M2 Pro 16GB?

Q: Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Granite 3.1 8B?

Not always. MacBook Pro M2 Pro 16GB 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.

YES — Tight Fit

B55Good

Estimated from fit model

Granite 3.1 8B needs ~9.5 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 9.5 GB, 35.5 tok/s, Tight fit

9.5 GB required11.5 GB available

83% VRAM used

Fit status

Tight fit

Decode

35.5 tok/s

TTFT

5459 ms

Safe context

33K

Memory

9.5 GB / 11.5 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on MacBook Pro M2 Pro 16GB
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	B	Runs well	35.5 tok/s	2977 ms	33K
Coding	B	Tight fit	35.5 tok/s	5459 ms	33K
Agentic Coding	B	Runs with offload	35.5 tok/s	7940 ms	33K
Reasoning	B	Tight fit	35.5 tok/s	6451 ms	33K
RAG	B	Runs with offload	35.5 tok/s	9925 ms	33K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	B56
NVFP4	4

Get started

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

Run

ollama run granite3.1-dense

Upgrade options

Hardware that runs Granite 3.1 8B well

MacBook Pro M3 Pro 18GBBudget pick

18 GB Unified (+2)

This setup is broadly balanced for this model.27.7 tok/s decode

~$1,999 MSRP

MacBook Pro M4 Pro 24GBBest value

24 GB Unified (+8)273 GB/s (+73)

Raises estimated decode speed by about 50%.53.3 tok/s decode

Raises estimated decode speed by about 50%.

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

~$1,999 MSRP

MacBook Pro M2 Max 32GBApple upgrade

32 GB Unified (+16)400 GB/s (+200)

Raises estimated decode speed by about 66%.58.8 tok/s decode

Raises estimated decode speed by about 66%.

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

~$1,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run Granite 3.1 8B?

Yes, MacBook Pro M2 Pro 16GB can run Granite 3.1 8B with a B grade (Tight fit). Expected decode speed: 35.5 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 9.5 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 16GB?

On MacBook Pro M2 Pro 16GB, Granite 3.1 8B achieves approximately 35.5 tokens per second decode speed with a time-to-first-token of 5459ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on MacBook Pro M2 Pro 16GB receives a B grade with 35.5 tok/s and 33K context.

What context window can Granite 3.1 8B use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite 3.1 8B can safely use up to 33K 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 16GB as fast as VRAM for Granite 3.1 8B?

See all results for MacBook Pro M2 Pro 16GB See all hardware for Granite 3.1 8B

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<iframe src="https://willitrunai.com/embed/granite-3.1-8b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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