Can Nemotron Nano 9B v2 run on MacBook Pro M2 Pro 16GB?

YES — Tight Fit

A79Great

Estimated from fit model

Nemotron Nano 9B v2 needs ~10.6 GB VRAM. MacBook Pro M2 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, 27.4 tok/s, Tight fit

10.6 GB required11.5 GB available

92% VRAM used

Fit status

Tight fit

Decode

27.4 tok/s

TTFT

7062 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 M2 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: 27.4 tok/s decode · 7.1s TTFT (warm) · 69 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	27.4 tok/s	3852 ms	22K
Coding	A	Tight fit	25.5 tok/s	7592 ms	22K
Agentic Coding	B	Very compromised (needs ~0.6 GB host RAM)	22.6 tok/s	12465 ms	22K
Reasoning	A	Tight fit	27.4 tok/s	8346 ms	22K
RAG	B	Very compromised (needs ~0.6 GB host RAM)	22.6 tok/s	15581 ms	22K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on MacBook Pro M2 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 M2 Pro 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3 14B	14B	A	13.8 tok/s
Ministral 3 14B	14B	B	13.7 tok/s

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run Nemotron Nano 9B v2?

Yes, MacBook Pro M2 Pro 16GB can run Nemotron Nano 9B v2 with a A grade (Tight fit). Expected decode speed: 25.5 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 9B v2?

The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 9B v2 run at on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Nemotron Nano 9B v2 achieves approximately 25.5 tokens per second decode speed with a time-to-first-token of 7592ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on MacBook Pro M2 Pro 16GB receives a A grade with 25.5 tok/s and 22K context.

What context window can Nemotron Nano 9B v2 use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Nemotron Nano 9B v2 can safely use up to 22K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron Nano 9B v2 feels slow on MacBook Pro M2 Pro 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Nemotron Nano 9B v2?

Not always. MacBook Pro M2 Pro 16GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for MacBook Pro M2 Pro 16GB See all hardware for Nemotron Nano 9B v2

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