Can Granite 3.1 8B run on MacBook Pro M3 Pro 18GB?

YES — Runs Great

B58Good

Estimated from fit model

Granite 3.1 8B needs ~9.7 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 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) — 9.7 GB, 27.7 tok/s, Runs well

9.7 GB required13.0 GB available

75% VRAM used

Fit status

Runs well

Decode

27.7 tok/s

TTFT

6979 ms

Safe context

43K

Memory

9.7 GB / 13.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.9 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on MacBook Pro M3 Pro 18GB
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	B	Runs well	27.7 tok/s	3807 ms	43K
Coding	B	Runs well	27.7 tok/s	6979 ms	43K
Agentic Coding	C	Tight fit	27.7 tok/s	10152 ms	43K
Reasoning	B	Runs well	27.7 tok/s	8248 ms	43K
RAG	C	Tight fit	27.7 tok/s	12689 ms	43K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C53
Q3_K_S	3	3.9 GB	Low	C54
NVFP4	4	4.5 GB	Medium	B55
Q4_K_M	4	4.9 GB	Medium	B56
Q5_K_M	5	5.8 GB	High	B57
Q6_K	6	6.6 GB	High	B57
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

升级选项

能流畅运行 Granite 3.1 8B 的硬件

Intel Arc A770 16GB经济之选

560 GB/s (+410)

Raises estimated decode speed by about 130%.63.8 tok/s 解码

Raises estimated decode speed by about 130%.

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

~$349 MSRP

RTX 5060 Ti 16GB最佳性价比

448 GB/s (+298)

Raises estimated decode speed by about 154%.70.4 tok/s 解码

Raises estimated decode speed by about 154%.

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

~$449 MSRP

Frequently asked questions

Can MacBook Pro M3 Pro 18GB run Granite 3.1 8B?

Yes, MacBook Pro M3 Pro 18GB can run Granite 3.1 8B with a B 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 9.7 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 18GB?

On MacBook Pro M3 Pro 18GB, 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 18GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on MacBook Pro M3 Pro 18GB receives a B grade with 27.7 tok/s and 43K context.

What context window can Granite 3.1 8B use on MacBook Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, Granite 3.1 8B can safely use up to 43K 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 18GB as fast as VRAM for Granite 3.1 8B?

Not always. MacBook Pro M3 Pro 18GB 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 M3 Pro 18GB See all hardware for Granite 3.1 8B

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