Can Granite 4.1 8B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

A73Great

Estimated from fit model

Granite 4.1 8B needs ~15.1 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~102 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 15.1 GB, 102.2 tok/s, Runs well

15.1 GB required46.1 GB available

33% VRAM used

Fit status

Runs well

Decode

102.2 tok/s

TTFT

1894 ms

Safe context

131K

Memory

15.1 GB / 46.1 GB

Memory breakdown

Weights4.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B on Mac Studio M2 Ultra 64GB
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: 102.2 tok/s decode · 1.9s TTFT (warm) · 256 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	A	Runs well	102.2 tok/s	1033 ms	131K
Coding	A	Runs well	102.2 tok/s	1894 ms	131K
Agentic Coding	A	Runs well	102.2 tok/s	2755 ms	131K
Reasoning	A	Runs well	102.2 tok/s	2238 ms	131K
RAG	A	Runs well	102.2 tok/s	3444 ms	131K

Quantization options

How Granite 4.1 8B (8B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B66
Q3_K_S	3	3.9 GB	Low	B66
NVFP4	4	4.5 GB	Medium	B66
Q4_K_M	4	4.9 GB	Medium	B66
Q5_K_M	5	5.8 GB	High	B66
Q6_K	6	6.6 GB	High	B66
Q8_0	8	8.6 GB	Very High	B67
F16Best for your GPU	16	16.4 GB	Maximum	B69

Get started

Copy-paste commands to run Granite 4.1 8B on your machine.

Run

ollama run granite4.1:8b

Your hardware

More models your Mac Studio M2 Ultra 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.2 tok/s
Qwen 3.5 27B	27B	S	30.4 tok/s
Qwen 3.6 27B	27B	S	23.1 tok/s
Qwen 3.6 35B A3B	35B	S	59 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	72.6 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run Granite 4.1 8B?

Yes, Mac Studio M2 Ultra 64GB can run Granite 4.1 8B with a A grade (Runs well). Expected decode speed: 102.2 tok/s.

How much VRAM does Granite 4.1 8B need?

Granite 4.1 8B (8B parameters) requires approximately 15.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 8B?

The recommended quantization for Granite 4.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 8B run at on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Granite 4.1 8B achieves approximately 102.2 tokens per second decode speed with a time-to-first-token of 1894ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run Granite 4.1 8B for coding?

For coding workloads, Granite 4.1 8B on Mac Studio M2 Ultra 64GB receives a A grade with 102.2 tok/s and 131K context.

What context window can Granite 4.1 8B use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Granite 4.1 8B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for Granite 4.1 8B?

Not always. Mac Studio M2 Ultra 64GB 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 Mac Studio M2 Ultra 64GB See all hardware for Granite 4.1 8B

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