Can Granite 4.1 8B run on MacBook Pro M1 Pro 16GB?

YES — Tight Fit

A74Great

○Estimated from fit model

Granite 4.1 8B needs ~9.9 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~29 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, 28.6 tok/s, Tight fit

9.9 GB required11.5 GB available

86% VRAM used

Fit status

Tight fit

Decode

28.6 tok/s

TTFT

6760 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 M1 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: 28.6 tok/s decode · 6.8s TTFT (warm) · 72 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	28.6 tok/s	3687 ms	26K
Coding	A	Tight fit	28.6 tok/s	6760 ms	26K
Agentic Coding	B	Runs with offload (needs ~0.3 GB host RAM)	25.2 tok/s	11168 ms	26K
Reasoning	A	Tight fit	28.6 tok/s	7990 ms	26K
RAG	B	Runs with offload (needs ~0.3 GB host RAM)	25.2 tok/s	13960 ms	26K

Quantization options

How Granite 4.1 8B (8B params) fits at each quantization level on MacBook Pro M1 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 M1 Pro 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	25.5 tok/s
👁 Alibaba Qwen 3 14B	14B	A	12.8 tok/s
👁 Mistral Ministral 3 14B	14B	B	12.7 tok/s
👁 NVIDIA Nemotron Nano 9B v2	9B	A	25.5 tok/s
👁 Tsinghua/Zhipu CodeGeeX 4 9B	9B	A	25.9 tok/s

Frequently asked questions

See all results for MacBook Pro M1 Pro 16GB See all hardware for Granite 4.1 8B

URL: https://willitrunai.com/can-run/granite-4.1-8b-on-m1-pro-16gb

⇱ Granite 4.1 8B on MacBook Pro M1 Pro 16GB? TIGHT FIT