Can Nemotron Cascade 2 30B A3B run on RTX PRO 5000 Blackwell 48GB?

YES — Runs Great

S92Excellent

○Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~27.2 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~162 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 27.2 GB, 174.5 tok/s, Runs well

27.2 GB required48.0 GB available

57% VRAM used

Fit status

Runs well

Decode

174.5 tok/s

TTFT

1109 ms

Safe context

129K

Memory

27.2 GB / 48.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsNemotron Cascade 2 30B A3B on RTX PRO 5000 Blackwell 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: 174.5 tok/s decode · 1.1s TTFT (warm) · 436 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	174.5 tok/s	605 ms	129K
Coding	S	Runs well	162.3 tok/s	1193 ms	129K
Agentic Coding	S	Runs well	174.5 tok/s	1614 ms	129K
Reasoning	S	Runs well	174.5 tok/s	1311 ms	129K
RAG	S	Runs well	174.5 tok/s	2017 ms	129K

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 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 Cascade 2 30B A3B on your machine.

Run

ollama run nemotron-cascade-2

Your hardware

More models your RTX PRO 5000 Blackwell 48GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	170.7 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	143.5 tok/s

Frequently asked questions

See all results for RTX PRO 5000 Blackwell 48GB See all hardware for Nemotron Cascade 2 30B A3B

URL: https://willitrunai.com/can-run/nemotron-cascade-2-30b-a3b-on-rtx-pro-5000-blackwell-48gb

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