Can Nemotron Nano 9B v2 run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

A79Great

○Estimated from fit model

Nemotron Nano 9B v2 needs ~15.7 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~91 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 15.7 GB, 90.9 tok/s, Runs well

15.7 GB required46.1 GB available

34% VRAM used

Fit status

Runs well

Decode

90.9 tok/s

TTFT

2131 ms

Safe context

131K

Memory

15.7 GB / 46.1 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on Mac Studio M2 Ultra 64GB
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: 90.9 tok/s decode · 2.1s TTFT (warm) · 227 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	90.9 tok/s	1162 ms	131K
Coding	A	Runs well	90.9 tok/s	2131 ms	131K
Agentic Coding	A	Runs well	90.9 tok/s	3099 ms	131K
Reasoning	A	Runs well	90.9 tok/s	2518 ms	131K
RAG	A	Runs well	90.9 tok/s	3874 ms	131K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A71
Q3_K_S	3	4.4 GB	Low	A71
NVFP4	4	5.0 GB	Medium	A71
Q4_K_M	4	5.5 GB	Medium	A71
Q5_K_M	5	6.5 GB	High	A72
Q6_K	6	7.4 GB	High	A72
Q8_0	8	9.6 GB	Very High	A72
F16Best for your GPU	16	18.5 GB	Maximum	A75

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 Studio M2 Ultra 64GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	70.2 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	30.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	23.1 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	59 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	72.6 tok/s

Frequently asked questions

See all results for Mac Studio M2 Ultra 64GB See all hardware for Nemotron Nano 9B v2

URL: https://willitrunai.com/can-run/nemotron-nano-9b-v2-on-m2-ultra-64gb

⇱ Nemotron Nano 9B v2 on Mac Studio M2 Ultra 64GB? YES