Is unified memory on Mac Studio M1 Ultra 128GB as fast as VRAM for Nemotron Cascade 2 30B A3B?

Not always. Mac Studio M1 Ultra 128GB 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 Studio M1 Ultra 128GB?

YES — Runs Great

S87Excellent

Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~36.0 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~63 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 36.0 GB, 68.0 tok/s, Runs well

36.0 GB required92.2 GB available

39% VRAM used

Fit status

Runs well

Decode

68.0 tok/s

TTFT

2846 ms

Safe context

262K

Memory

36.0 GB / 92.2 GB

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsNemotron Cascade 2 30B A3B on Mac Studio M1 Ultra 128GB
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: 68.0 tok/s decode · 2.8s TTFT (warm) · 170 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	68.0 tok/s	1553 ms	262K
Coding	S	Runs well	63.3 tok/s	3060 ms	262K
Agentic Coding	S	Runs well	68.0 tok/s	4140 ms	262K
Reasoning	S	Runs well	68.0 tok/s	3364 ms	262K
RAG	S	Runs well	68.0 tok/s	5175 ms	262K

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A78
Q3_K_S	3	14.7 GB	Low	A78
NVFP4	4

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 Mac Studio M1 Ultra 128GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	6 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can Mac Studio M1 Ultra 128GB run Nemotron Cascade 2 30B A3B?

Yes, Mac Studio M1 Ultra 128GB can run Nemotron Cascade 2 30B A3B with a S grade (Runs well). Expected decode speed: 63.3 tok/s.

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

Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 36.0 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 Mac Studio M1 Ultra 128GB?

On Mac Studio M1 Ultra 128GB, Nemotron Cascade 2 30B A3B achieves approximately 63.3 tokens per second decode speed with a time-to-first-token of 3060ms using Q4_K_M quantization.

Can Mac Studio M1 Ultra 128GB run Nemotron Cascade 2 30B A3B for coding?

For coding workloads, Nemotron Cascade 2 30B A3B on Mac Studio M1 Ultra 128GB receives a S grade with 63.3 tok/s and 262K context.

What context window can Nemotron Cascade 2 30B A3B use on Mac Studio M1 Ultra 128GB?

On Mac Studio M1 Ultra 128GB, Nemotron Cascade 2 30B A3B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for Mac Studio M1 Ultra 128GB See all hardware for Nemotron Cascade 2 30B A3B

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