Can Nemotron Cascade 2 30B A3B run on MacBook Pro M4 Pro 48GB?

YES — Runs Great

S91Excellent

Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~27.3 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~33 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) — 27.3 GB, 32.5 tok/s, Runs well

27.3 GB required34.6 GB available

79% VRAM used

Fit status

Runs well

Decode

32.5 tok/s

TTFT

5958 ms

Safe context

56K

Memory

27.3 GB / 34.6 GB

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsNemotron Cascade 2 30B A3B on MacBook Pro M4 Pro 48GB
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: 32.5 tok/s decode · 6.0s TTFT (warm) · 81 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	32.5 tok/s	3250 ms	56K
Coding	S	Runs well	32.5 tok/s	5958 ms	56K
Agentic Coding	S	Tight fit	32.5 tok/s	8666 ms	56K
Reasoning	S	Runs well	32.5 tok/s	7041 ms	56K
RAG	S	Tight fit	32.5 tok/s	10832 ms	56K

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A84
Q3_K_S	3	14.7 GB	Low	S85
NVFP4	4	16.8 GB	Medium	S86
Q4_K_M	4	18.3 GB	Medium	S87
Q5_K_M	5	21.6 GB	High	S87
Q6_KBest for your GPU	6	24.6 GB	High	S86
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Get started

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

Run

ollama run nemotron-cascade-2

Your hardware

More models your MacBook Pro M4 Pro 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	31.8 tok/s
Qwen 3.6 35B A3B	35B	S	29.4 tok/s
Qwen 3.5 35B A3B	35B	S	32 tok/s
Qwen 3 32B	32B	S	21.1 tok/s
Qwen 3 30B A3B	30.5B	S	31.8 tok/s

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run Nemotron Cascade 2 30B A3B?

Yes, MacBook Pro M4 Pro 48GB can run Nemotron Cascade 2 30B A3B with a S grade (Runs well). Expected decode speed: 32.5 tok/s.

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

Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 27.3 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Nemotron Cascade 2 30B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Cascade 2 30B A3B run at on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, Nemotron Cascade 2 30B A3B achieves approximately 32.5 tokens per second decode speed with a time-to-first-token of 5958ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run Nemotron Cascade 2 30B A3B for coding?

For coding workloads, Nemotron Cascade 2 30B A3B on MacBook Pro M4 Pro 48GB receives a S grade with 32.5 tok/s and 56K context.

What context window can Nemotron Cascade 2 30B A3B use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, Nemotron Cascade 2 30B A3B can safely use up to 56K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for Nemotron Cascade 2 30B A3B?

Not always. MacBook Pro M4 Pro 48GB 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 M4 Pro 48GB See all hardware for Nemotron Cascade 2 30B A3B

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