Can Nemotron Cascade 2 30B A3B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

S93Excellent

Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~25.3 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~58 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 25.3 GB, 58.4 tok/s, Runs well

25.3 GB required32.0 GB available

79% VRAM used

Fit status

Runs well

Decode

58.4 tok/s

TTFT

3317 ms

Safe context

52K

Memory

25.3 GB / 32.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsNemotron Cascade 2 30B A3B on Radeon AI PRO R9700 32GB
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: 58.4 tok/s decode · 3.3s TTFT (warm) · 146 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	58.4 tok/s	1809 ms	52K
Coding	S	Runs well	58.4 tok/s	3317 ms	52K
Agentic Coding	S	Tight fit	58.4 tok/s	4824 ms	52K
Reasoning	S	Runs well	58.4 tok/s	3920 ms	52K
RAG	S	Tight fit	58.4 tok/s	6030 ms	52K

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A85
Q3_K_S	3	14.7 GB	Low	S86
NVFP4	4	16.8 GB	Medium	S87
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 Radeon AI PRO R9700 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	57.1 tok/s
Qwen 3.6 35B A3B	35B	S	48 tok/s
Qwen 3.5 35B A3B	35B	S	52.2 tok/s
Qwen 3 32B	32B	S	21 tok/s
Qwen 3 30B A3B	30.5B	S	57.1 tok/s

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Nemotron Cascade 2 30B A3B?

Yes, Radeon AI PRO R9700 32GB can run Nemotron Cascade 2 30B A3B with a S grade (Runs well). Expected decode speed: 58.4 tok/s.

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

Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 25.3 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 Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Nemotron Cascade 2 30B A3B achieves approximately 58.4 tokens per second decode speed with a time-to-first-token of 3317ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Nemotron Cascade 2 30B A3B for coding?

For coding workloads, Nemotron Cascade 2 30B A3B on Radeon AI PRO R9700 32GB receives a S grade with 58.4 tok/s and 52K context.

What context window can Nemotron Cascade 2 30B A3B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Nemotron Cascade 2 30B A3B can safely use up to 52K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for Nemotron Cascade 2 30B A3B

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