Can Granite 3.1 8B run on MacBook Pro M4 Pro 24GB?

YES — Runs Great

B57Good

○Estimated from fit model

Granite 3.1 8B needs ~10.3 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~50 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) — 10.3 GB, 53.3 tok/s, Runs well

10.3 GB required17.3 GB available

60% VRAM used

Fit status

Runs well

Decode

53.3 tok/s

TTFT

3636 ms

Safe context

73K

Memory

10.3 GB / 17.3 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on MacBook Pro M4 Pro 24GB
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: 53.3 tok/s decode · 3.6s TTFT (warm) · 133 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	B	Runs well	53.3 tok/s	1983 ms	73K
Coding	B	Runs well	49.5 tok/s	3908 ms	73K
Agentic Coding	B	Runs well	53.3 tok/s	5288 ms	73K
Reasoning	B	Runs well	53.3 tok/s	4297 ms	73K
RAG	B	Runs well	53.3 tok/s	6610 ms	73K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C51
Q3_K_S	3	3.9 GB	Low	C52
NVFP4	4

Get started

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

Run

ollama run granite3.1-dense

Frequently asked questions

See all results for MacBook Pro M4 Pro 24GB See all hardware for Granite 3.1 8B

URL: https://willitrunai.com/can-run/granite-3.1-8b-on-m4-pro-24gb

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