Can Granite 3.1 8B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

C51Usable

Estimated from fit model

Granite 3.1 8B needs ~18.1 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

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) — 18.1 GB, 58.8 tok/s, Runs well

18.1 GB required69.1 GB available

26% VRAM used

Fit status

Runs well

Decode

58.8 tok/s

TTFT

3294 ms

Safe context

128K

Memory

18.1 GB / 69.1 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on MacBook Pro M2 Max 96GB
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: 58.8 tok/s decode · 3.3s TTFT (warm) · 147 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	58.8 tok/s	1797 ms	128K
Coding	C	Runs well	58.8 tok/s	3294 ms	128K
Agentic Coding	C	Runs well	58.8 tok/s	4791 ms	128K
Reasoning	C	Runs well	58.8 tok/s	3893 ms	128K
RAG	C	Runs well	58.8 tok/s	5989 ms	128K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C45
NVFP4	4	4.5 GB	Medium	C45
Q4_K_M	4	4.9 GB	Medium	C45
Q5_K_M	5	5.8 GB	High	C45
Q6_K	6	6.6 GB	High	C45
Q8_0	8	8.6 GB	Very High	C45
F16Best for your GPU	16	16.4 GB	Maximum	C47

Get started

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

Run

ollama run granite3.1-dense

Upgrade-Optionen

Hardware, die Granite 3.1 8B gut ausführt

Mac Studio M2 Ultra 128GBGünstige Wahl

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 90%.112 tok/s Dekodierung

Raises estimated decode speed by about 90%.

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

ca. $3,999 MSRP

Mac Studio M1 Ultra 128GBBestes Preis-Leistungs-Verhältnis

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 90%.111.5 tok/s Dekodierung

Raises estimated decode speed by about 90%.

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

ca. $3,999 MSRP

MacBook Pro M4 Max 128GBApple-Upgrade

128 GB Unified (+32)546 GB/s (+146)

Raises estimated decode speed by about 48%.87.1 tok/s Dekodierung

Raises estimated decode speed by about 48%.

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

ca. $4,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Granite 3.1 8B?

Yes, MacBook Pro M2 Max 96GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 58.8 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 18.1 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 Max 96GB?

On MacBook Pro M2 Max 96GB, Granite 3.1 8B achieves approximately 58.8 tokens per second decode speed with a time-to-first-token of 3294ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on MacBook Pro M2 Max 96GB receives a C grade with 58.8 tok/s and 128K context.

What context window can Granite 3.1 8B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, 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 M2 Max 96GB as fast as VRAM for Granite 3.1 8B?

Not always. MacBook Pro M2 Max 96GB 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 Max 96GB See all hardware for Granite 3.1 8B

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