Can Nemotron Nano 9B v2 run on MacBook Pro M3 Pro 18GB?

YES — Tight Fit

A79Great

○Estimated from fit model

Nemotron Nano 9B v2 needs ~10.8 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~21 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) — 10.8 GB, 21.4 tok/s, Tight fit

10.8 GB required13.0 GB available

83% VRAM used

Fit status

Tight fit

Decode

21.4 tok/s

TTFT

9029 ms

Safe context

30K

Memory

10.8 GB / 13.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.9 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on MacBook Pro M3 Pro 18GB
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: 21.4 tok/s decode · 9.0s TTFT (warm) · 54 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	21.4 tok/s	4925 ms	30K
Coding	A	Tight fit	21.4 tok/s	9029 ms	30K
Agentic Coding	A	Runs with offload (needs ~0.1 GB host RAM)	20.5 tok/s	13720 ms	30K
Reasoning	A	Tight fit	21.4 tok/s	10671 ms	30K
RAG	A	Runs with offload (needs ~0.1 GB host RAM)	20.5 tok/s	17150 ms	30K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A79
Q3_K_S	3	4.4 GB	Low	A80
NVFP4	4	5.0 GB	Medium	A80
Q4_K_M	4	5.5 GB	Medium	A81
Q5_K_M	5	6.5 GB	High	A82
Q6_K	6	7.4 GB	High	A81
Q8_0Best for your GPU	8	9.6 GB	Very High	A81
F16	16	18.5 GB	Maximum	F0

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 MacBook Pro M3 Pro 18GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 14B	14B	A	12.3 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	A	10.6 tok/s
👁 Mistral Ministral 3 14B	14B	A	12.3 tok/s
👁 Microsoft Phi-4 14B	14B	B	11.6 tok/s
👁 Alibaba Qwen 2.5 14B	14B	B	11.7 tok/s

Frequently asked questions

See all results for MacBook Pro M3 Pro 18GB See all hardware for Nemotron Nano 9B v2

URL: https://willitrunai.com/can-run/nemotron-nano-9b-v2-on-m3-pro-18gb

⇱ Nemotron Nano 9B v2 on MacBook Pro M3 Pro 18GB? TIGHT FIT