Can Granite 3.1 8B run on MacBook Pro M3 Pro 36GB?

Q: Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for Granite 3.1 8B?

Not always. MacBook Pro M3 Pro 36GB 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 — Runs Great

C52Usable

Estimated from fit model

Granite 3.1 8B needs ~11.6 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~28 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) — 11.6 GB, 27.7 tok/s, Runs well

11.6 GB required25.9 GB available

45% VRAM used

Fit status

Runs well

Decode

27.7 tok/s

TTFT

6979 ms

Safe context

128K

Memory

11.6 GB / 25.9 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on MacBook Pro M3 Pro 36GB
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: 27.7 tok/s decode · 7.0s TTFT (warm) · 69 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	27.7 tok/s	3807 ms	128K
Coding	C	Runs well	27.7 tok/s	6979 ms	128K
Agentic Coding	C	Runs well	27.7 tok/s	10152 ms	128K
Reasoning	C	Runs well	27.7 tok/s	8248 ms	128K
RAG	C	Runs well	27.7 tok/s	12689 ms	128K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C49
Q3_K_S	3	3.9 GB	Low	C49
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 M4 Max 48GBBudget pick

48 GB Unified (+12)546 GB/s (+396)

Raises estimated decode speed by about 214%.87.1 tok/s decode

Raises estimated decode speed by about 214%.

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

~$2,499 MSRP

MacBook Pro M3 Max 48GBBest value

48 GB Unified (+12)400 GB/s (+250)

Raises estimated decode speed by about 119%.60.8 tok/s decode

Raises estimated decode speed by about 119%.

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

~$2,499 MSRP

MacBook Pro M4 Pro 48GBApple upgrade

48 GB Unified (+12)273 GB/s (+123)

Raises estimated decode speed by about 92%.53.3 tok/s decode

Raises estimated decode speed by about 92%.

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

~$2,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Pro 36GB run Granite 3.1 8B?

Yes, MacBook Pro M3 Pro 36GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 27.7 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 11.6 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 M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, Granite 3.1 8B achieves approximately 27.7 tokens per second decode speed with a time-to-first-token of 6979ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 36GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on MacBook Pro M3 Pro 36GB receives a C grade with 27.7 tok/s and 128K context.

What context window can Granite 3.1 8B use on MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, Granite 3.1 8B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for Granite 3.1 8B?

See all results for MacBook Pro M3 Pro 36GB See all hardware for Granite 3.1 8B

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