Can Nemotron Nano 9B v2 run on Mac mini M4 32GB?

YES — Runs Great

A77Great

▲Estimated — low-sample bucket· few comparable runs

Nemotron Nano 9B v2 needs ~12.3 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~17 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) — 12.3 GB, 16.8 tok/s, Runs well

12.3 GB required23.0 GB available

53% VRAM used

Fit status

Runs well

Decode

16.8 tok/s

TTFT

11517 ms

Safe context

86K

Memory

12.3 GB / 23.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on Mac mini M4 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: 16.8 tok/s decode · 11.5s TTFT (warm) · 42 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	16.8 tok/s	6282 ms	86K
Coding	A	Runs well	16.8 tok/s	11517 ms	86K
Agentic Coding	A	Runs well	16.8 tok/s	16752 ms	86K
Reasoning	A	Runs well	16.8 tok/s	13611 ms	86K
RAG	A	Runs well	16.8 tok/s	20940 ms	86K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A74
Q3_K_S	3	4.4 GB	Low	A75
NVFP4	4	5.0 GB	Medium	A75
Q4_K_M	4	5.5 GB	Medium	A75
Q5_K_M	5	6.5 GB	High	A76
Q6_K	6	7.4 GB	High	A77
Q8_0	8	9.6 GB	Very High	A78
F16Best for your GPU	16	18.5 GB	Maximum	A79

Get started

Copy-paste commands to run Nemotron Nano 9B v2 on your machine.

Run

ollama run nemotron-nano:9b-v2

Your hardware

More models your Mac mini M4 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	A	11.7 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	8.6 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	7.1 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	12.4 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	A	10.2 tok/s

Frequently asked questions

See all results for Mac mini M4 32GB See all hardware for Nemotron Nano 9B v2

URL: https://willitrunai.com/can-run/nemotron-nano-9b-v2-on-m4-mini-32gb

⇱ Nemotron Nano 9B v2 on Mac mini M4 32GB? YES