Can Granite 4.1 8B run on MacBook Pro M2 Pro 16GB?

YES — Tight Fit

A75Great

Estimated from fit model

Granite 4.1 8B needs ~9.9 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~31 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) — 9.9 GB, 30.8 tok/s, Tight fit

9.9 GB required11.5 GB available

86% VRAM used

Fit status

Tight fit

Decode

30.8 tok/s

TTFT

6278 ms

Safe context

26K

Memory

9.9 GB / 11.5 GB

Memory breakdown

Weights4.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsGranite 4.1 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: 30.8 tok/s decode · 6.3s TTFT (warm) · 77 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	30.8 tok/s	3424 ms	26K
Coding	A	Tight fit	30.8 tok/s	6278 ms	26K
Agentic Coding	B	Runs with offload (needs ~0.3 GB host RAM)	27.2 tok/s	10370 ms	26K
Reasoning	A	Tight fit	30.8 tok/s	7419 ms	26K
RAG	B	Runs with offload (needs ~0.3 GB host RAM)	27.2 tok/s	12963 ms	26K

Quantization options

How Granite 4.1 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	A74
Q3_K_S	3	3.9 GB	Low	A75
NVFP4	4	4.5 GB	Medium	A76
Q4_K_M	4	4.9 GB	Medium	A77
Q5_K_M	5	5.8 GB	High	A77
Q6_KBest for your GPU	6	6.6 GB	High	A76
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

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 MacBook Pro M2 Pro 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	27.4 tok/s
Qwen 3 14B	14B	A	13.8 tok/s
Ministral 3 14B	14B	B	13.7 tok/s
Nemotron Nano 9B v2	9B	A	27.4 tok/s
CodeGeeX 4 9B	9B	A	27.9 tok/s

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run Granite 4.1 8B?

Yes, MacBook Pro M2 Pro 16GB can run Granite 4.1 8B with a A grade (Tight fit). Expected decode speed: 30.8 tok/s.

How much VRAM does Granite 4.1 8B need?

Granite 4.1 8B (8B parameters) requires approximately 9.9 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 MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite 4.1 8B achieves approximately 30.8 tokens per second decode speed with a time-to-first-token of 6278ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run Granite 4.1 8B for coding?

For coding workloads, Granite 4.1 8B on MacBook Pro M2 Pro 16GB receives a A grade with 30.8 tok/s and 26K context.

What context window can Granite 4.1 8B use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite 4.1 8B can safely use up to 26K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Granite 4.1 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.

See all results for MacBook Pro M2 Pro 16GB See all hardware for Granite 4.1 8B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/granite-4.1-8b-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>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

30.8 tok/s

3424 ms

26K

Coding

Tight fit

30.8 tok/s

6278 ms

26K

Agentic Coding

Runs with offload (needs ~0.3 GB host RAM)

27.2 tok/s

10370 ms

26K

Reasoning

Tight fit

30.8 tok/s

7419 ms

26K

RAG

Runs with offload (needs ~0.3 GB host RAM)

27.2 tok/s

12963 ms

26K

Quantization options

How Granite 4.1 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	A74
Q3_K_S	3	3.9 GB	Low	A75
NVFP4	4	4.5 GB	Medium	A76
Q4_K_M	4	4.9 GB	Medium	A77
Q5_K_M	5	5.8 GB	High	A77
Q6_KBest for your GPU	6	6.6 GB	High	A76
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

27.4 tok/s

Qwen 3 14B

14B

13.8 tok/s

Ministral 3 14B

14B

13.7 tok/s

Nemotron Nano 9B v2

27.4 tok/s

CodeGeeX 4 9B

27.9 tok/s

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run Granite 4.1 8B?

Yes, MacBook Pro M2 Pro 16GB can run Granite 4.1 8B with a A grade (Tight fit). Expected decode speed: 30.8 tok/s.

How much VRAM does Granite 4.1 8B need?

Granite 4.1 8B (8B parameters) requires approximately 9.9 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 MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite 4.1 8B achieves approximately 30.8 tokens per second decode speed with a time-to-first-token of 6278ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run Granite 4.1 8B for coding?

For coding workloads, Granite 4.1 8B on MacBook Pro M2 Pro 16GB receives a A grade with 30.8 tok/s and 26K context.

What context window can Granite 4.1 8B use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite 4.1 8B can safely use up to 26K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Granite 4.1 8B?