Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for Nemotron 3 Nano 30B?

Not always. MacBook Pro M2 Max 96GB 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 3 Nano 30B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

S86Excellent

Estimated from fit model

Nemotron 3 Nano 30B needs ~32.0 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 32.0 GB, 13.6 tok/s, Runs well

32.0 GB required69.1 GB available

46% VRAM used

Fit status

Runs well

Decode

13.6 tok/s

TTFT

14206 ms

Safe context

131K

Memory

32.0 GB / 69.1 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B on MacBook Pro M2 Max 96GB
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: 13.6 tok/s decode · 14.2s TTFT (warm) · 34 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	S	Runs well	13.6 tok/s	7749 ms	131K
Coding	S	Runs well	13.6 tok/s	14206 ms	131K
Agentic Coding	S	Runs well	13.6 tok/s	20663 ms	131K
Reasoning	S	Runs well	13.6 tok/s	16788 ms	131K
RAG	S	Runs well	13.6 tok/s	25828 ms	131K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A81
Q3_K_S	3	14.7 GB	Low	A82
NVFP4	4

Get started

Copy-paste commands to run Nemotron 3 Nano 30B on your machine.

Run

ollama run nemotron-nano:30b

Your hardware

More models your MacBook Pro M2 Max 96GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	35.1 tok/s
Qwen 3.6 35B A3B	35B	S	32.4 tok/s

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Nemotron 3 Nano 30B?

Yes, MacBook Pro M2 Max 96GB can run Nemotron 3 Nano 30B with a S grade (Runs well). Expected decode speed: 13.6 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 32.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron 3 Nano 30B?

The recommended quantization for Nemotron 3 Nano 30B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron 3 Nano 30B run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Nemotron 3 Nano 30B achieves approximately 13.6 tokens per second decode speed with a time-to-first-token of 14206ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on MacBook Pro M2 Max 96GB receives a S grade with 13.6 tok/s and 131K context.

What context window can Nemotron 3 Nano 30B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Nemotron 3 Nano 30B can safely use up to 131K 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 Max 96GB See all hardware for Nemotron 3 Nano 30B

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