Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for Nemotron Nano 9B v2?

Not always. MacBook Pro M2 Pro 32GB 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.

Can Nemotron Nano 9B v2 run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

A79Great

Estimated from fit model

Nemotron Nano 9B v2 needs ~12.3 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~27 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, 27.4 tok/s, Runs well

12.3 GB required23.0 GB available

53% VRAM used

Fit status

Runs well

Decode

27.4 tok/s

TTFT

7062 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 MacBook Pro M2 Pro 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: 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.

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	27.4 tok/s	3852 ms	86K
Coding	A	Runs well	27.4 tok/s	7062 ms	86K
Agentic Coding	A	Runs well	27.4 tok/s	10272 ms	86K
Reasoning	A	Runs well	27.4 tok/s	8346 ms	86K
RAG	A	Runs well	27.4 tok/s	12840 ms	86K

Quantization options

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

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 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	19 tok/s
Qwen 3.5 27B	27B	S	8.5 tok/s

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run Nemotron Nano 9B v2?

Yes, MacBook Pro M2 Pro 32GB can run Nemotron Nano 9B v2 with a A grade (Runs well). Expected decode speed: 27.4 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 12.3 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 32GB?

On MacBook Pro M2 Pro 32GB, Nemotron Nano 9B v2 achieves approximately 27.4 tokens per second decode speed with a time-to-first-token of 7062ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 32GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on MacBook Pro M2 Pro 32GB receives a A grade with 27.4 tok/s and 86K context.

What context window can Nemotron Nano 9B v2 use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Nemotron Nano 9B v2 can safely use up to 86K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for MacBook Pro M2 Pro 32GB See all hardware for Nemotron Nano 9B v2

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/nemotron-nano-9b-v2-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: