Can Nemotron Nano 9B v2 run on MacBook Pro M1 Pro 16GB?

YES — Tight Fit

A79Great

○Estimated from fit model

Nemotron Nano 9B v2 needs ~10.6 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~26 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) — 10.6 GB, 25.5 tok/s, Tight fit

10.6 GB required11.5 GB available

92% VRAM used

Fit status

Tight fit

Decode

25.5 tok/s

TTFT

7605 ms

Safe context

22K

Memory

10.6 GB / 11.5 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 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: 25.5 tok/s decode · 7.6s TTFT (warm) · 64 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	25.5 tok/s	4148 ms	22K
Coding	A	Tight fit	25.5 tok/s	7605 ms	22K
Agentic Coding	B	Very compromised (needs ~0.6 GB host RAM)	21.0 tok/s	13423 ms	22K
Reasoning	A	Tight fit	25.5 tok/s	8988 ms	22K
RAG	B	Very compromised (needs ~0.6 GB host RAM)	21.0 tok/s	16779 ms	22K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A80
Q3_K_S	3	4.4 GB	Low	A81
NVFP4	4	5.0 GB	Medium	A82
Q4_K_M	4	5.5 GB	Medium	A82
Q5_K_M	5	6.5 GB	High	A82
Q6_KBest for your GPU	6	7.4 GB	High	A81
Q8_0	8	9.6 GB	Very High	F0
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 M1 Pro 16GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3 14B	14B	A	12.8 tok/s
👁 Mistral Ministral 3 14B	14B	B	12.7 tok/s

Frequently asked questions

See all results for MacBook Pro M1 Pro 16GB See all hardware for Nemotron Nano 9B v2

URL: https://willitrunai.com/can-run/nemotron-nano-9b-v2-on-m1-pro-16gb

⇱ Nemotron Nano 9B v2 on MacBook Pro M1 Pro 16GB? TIGHT FIT