Can Granite 4.1 8B run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

A73Great

○Estimated from fit model

Granite 4.1 8B needs ~11.7 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~31 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) — 11.7 GB, 30.8 tok/s, Runs well

11.7 GB required23.0 GB available

51% VRAM used

Fit status

Runs well

Decode

30.8 tok/s

TTFT

6278 ms

Safe context

90K

Memory

11.7 GB / 23.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B on MacBook Pro M2 Pro 32GB
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	90K
Coding	A	Runs well	30.8 tok/s	6278 ms	90K
Agentic Coding	A	Runs well	30.8 tok/s	9131 ms	90K
Reasoning	A	Runs well	30.8 tok/s	7419 ms	90K
RAG	A	Runs well	30.8 tok/s	11414 ms	90K

Quantization options

How Granite 4.1 8B (8B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B69
Q3_K_S	3	3.9 GB	Low	B69
NVFP4	4	4.5 GB	Medium	B70
Q4_K_M	4	4.9 GB	Medium	B70
Q5_K_M	5	5.8 GB	High	A70
Q6_K	6	6.6 GB	High	A71
Q8_0	8	8.6 GB	Very High	A72
F16Best for your GPU	16	16.4 GB	Maximum	A74

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 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	A	19 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	8.5 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	7 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	20.1 tok/s
👁 Alibaba Qwen 3.5 9B	9B	S	27.4 tok/s

Frequently asked questions

See all results for MacBook Pro M2 Pro 32GB See all hardware for Granite 4.1 8B

URL: https://willitrunai.com/can-run/granite-4.1-8b-on-m2-pro-32gb

⇱ Granite 4.1 8B on MacBook Pro M2 Pro 32GB? YES