Can Nemotron Cascade 2 30B A3B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

S87Excellent

○Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~32.5 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~36 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) — 32.5 GB, 35.9 tok/s, Runs well

32.5 GB required69.1 GB available

47% VRAM used

Fit status

Runs well

Decode

35.9 tok/s

TTFT

5398 ms

Safe context

216K

Memory

32.5 GB / 69.1 GB

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsNemotron Cascade 2 30B A3B 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: 35.9 tok/s decode · 5.4s TTFT (warm) · 90 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	35.9 tok/s	2944 ms	216K
Coding	S	Runs well	35.9 tok/s	5398 ms	216K
Agentic Coding	S	Runs well	35.9 tok/s	7852 ms	216K
Reasoning	S	Runs well	35.9 tok/s	6380 ms	216K
RAG	S	Runs well	35.9 tok/s	9815 ms	216K

Quantization options

How Nemotron Cascade 2 30B A3B (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	A79
Q3_K_S	3	14.7 GB	Low	A80
NVFP4	4	16.8 GB	Medium	A80
Q4_K_M	4	18.3 GB	Medium	A80
Q5_K_M	5	21.6 GB	High	A81
Q6_K	6	24.6 GB	High	A82
Q8_0Best for your GPU	8	32.1 GB	Very High	A84
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 M2 Max 96GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	35.1 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	32.4 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	35.3 tok/s
👁 Alibaba Qwen 3 32B	32B	S	12.9 tok/s
👁 Alibaba Qwen 3 30B A3B	30.5B	S	35.1 tok/s

Frequently asked questions

See all results for MacBook Pro M2 Max 96GB See all hardware for Nemotron Cascade 2 30B A3B

URL: https://willitrunai.com/can-run/nemotron-cascade-2-30b-a3b-on-m2-max-96gb

⇱ Nemotron Cascade 2 30B A3B on MacBook Pro M2 Max 96GB? YES