Is unified memory on Mac mini M2 24GB as fast as VRAM for Nemotron Cascade 2 30B A3B?

Not always. Mac mini M2 24GB 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 Cascade 2 30B A3B run on Mac mini M2 24GB?

Q: What should I upgrade first if Nemotron Cascade 2 30B A3B feels slow on Mac mini M2 24GB?

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

YES — With Q2_K

S85Excellent

Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~18.1 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q2_K quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

Nemotron Cascade 2 30B A3B at Q4_K_M needs 24.7 GB — too much for Mac mini M2 24GB (17.3 GB). Runs at Q2_K (18.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 24.7 GB, exceeds 17.3 GB available

24.7 GB required17.3 GB available

143% VRAM needed

7.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.2 tok/s

TTFT

31213 ms

Safe context

Memory

24.7 GB / 17.3 GB

Offload

30%

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron Cascade 2 30B A3B on Mac mini M2 24GB
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: 6.2 tok/s decode · 31.2s TTFT (warm) · 16 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	F	Too heavy	6.7 tok/s	15857 ms	4K
Coding	F	Too heavy	6.2 tok/s	31213 ms	4K
Agentic Coding	F	Too heavy	5.5 tok/s	51549 ms	4K
Reasoning	F	Too heavy	6.2 tok/s	36888 ms	4K
RAG	F	Too heavy	5.5 tok/s	64437 ms	4K

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	11.7 GB	Low	S88
Q3_K_S	3	14.7 GB	Low	F0

Get started

Copy-paste commands to run Nemotron Cascade 2 30B A3B on your machine.

Run

ollama run nemotron-cascade-2

Upgrade options

Hardware that runs Nemotron Cascade 2 30B A3B well

MacBook Pro M4 32GBBest value

32 GB Unified (+8)120 GB/s (+20)

Makes the model fit on the accelerator instead of staying completely out of reach.11.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 81%.

~$799 MSRP

Mac mini M4 64GBBudget pick

64 GB Unified (+40)120 GB/s (+20)

Makes the model fit on the accelerator instead of staying completely out of reach.13.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

Mac mini M4 32GBApple upgrade

32 GB Unified (+8)120 GB/s (+20)

Makes the model fit on the accelerator instead of staying completely out of reach.11.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 81%.

~$1,099 MSRP

RTX 5090 32GBBiggest leap

32 GB VRAM (+8)1792 GB/s (+1692)

Makes the model fit on the accelerator instead of staying completely out of reach.185.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run Nemotron Cascade 2 30B A3B?

Yes, Mac mini M2 24GB can run Nemotron Cascade 2 30B A3B at Q2_K quantization (Runs with offload (needs ~0.5 GB host RAM)). The recommended Q4_K_M requires 24.7 GB which exceeds available memory, but at Q2_K it needs only 18.1 GB. Expected decode speed: 12.2 tok/s.

How much VRAM does Nemotron Cascade 2 30B A3B need?

Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 24.7 GB at Q4_K_M quantization. On Mac mini M2 24GB, it fits at Q2_K using 18.1 GB.

What is the best quantization for Nemotron Cascade 2 30B A3B?

The recommended quantization is Q4_K_M, but on Mac mini M2 24GB the best fitting quantization is Q2_K, which uses 18.1 GB.

What speed will Nemotron Cascade 2 30B A3B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Nemotron Cascade 2 30B A3B achieves approximately 12.2 tokens per second decode speed with a time-to-first-token of 15853ms using Q2_K quantization.

Can Mac mini M2 24GB run Nemotron Cascade 2 30B A3B for coding?

For coding workloads, Nemotron Cascade 2 30B A3B on Mac mini M2 24GB receives a F grade with 6.2 tok/s and 4K context.

What context window can Nemotron Cascade 2 30B A3B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Nemotron Cascade 2 30B A3B can safely use up to 11K tokens of context at Q2_K quantization. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for Mac mini M2 24GB See all hardware for Nemotron Cascade 2 30B A3B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/nemotron-cascade-2-30b-a3b-on-m2-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: