Can Nemotron Cascade 2 30B A3B run on Radeon PRO W7700 16GB?

YES — With Q2_K

A79Great

Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~17.1 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q2_K quantization, expect ~45 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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.

Nemotron Cascade 2 30B A3B at Q4_K_M needs 23.7 GB — too much for Radeon PRO W7700 16GB (16.0 GB). Runs at Q2_K (17.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 23.7 GB, exceeds 16.0 GB available

23.7 GB required16.0 GB available

148% VRAM needed

7.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

17.2 tok/s

TTFT

11265 ms

Safe context

Memory

23.7 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron Cascade 2 30B A3B on Radeon PRO W7700 16GB
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: 17.2 tok/s decode · 11.3s TTFT (warm) · 43 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	18.3 tok/s	5776 ms	4K
Coding	F	Too heavy	16.0 tok/s	12109 ms	4K
Agentic Coding	F	Too heavy	12.5 tok/s	22505 ms	4K
Reasoning	F	Too heavy	16.0 tok/s	14311 ms	4K
RAG	F	Too heavy	12.5 tok/s	28131 ms	4K

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	F0
Q3_K_S	3	14.7 GB	Low	F0
NVFP4	4	16.8 GB	Medium	F0
Q4_K_M	4	18.3 GB	Medium	F0
Q5_K_M	5	21.6 GB	High	F0
Q6_K	6	24.6 GB	High	F0
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

Upgrade-Optionen

Hardware, die Nemotron Cascade 2 30B A3B gut ausführt

RX 7900 XTX 24GBGünstige Wahl

24 GB VRAM (+8)960 GB/s (+384)

Makes the model fit on the accelerator instead of staying completely out of reach.76.5 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $999 MSRP

Radeon AI PRO R9700 32GBBestes Preis-Leistungs-Verhältnis

32 GB VRAM (+16)640 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.58.4 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $1,899 MSRP

Radeon Pro W6800 32GBAMD-Upgrade

32 GB VRAM (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.44.3 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $2,249 MSRP

Frequently asked questions

Can Radeon PRO W7700 16GB run Nemotron Cascade 2 30B A3B?

Yes, Radeon PRO W7700 16GB can run Nemotron Cascade 2 30B A3B at Q2_K quantization (Runs with offload (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 23.7 GB which exceeds available memory, but at Q2_K it needs only 17.1 GB. Expected decode speed: 45.4 tok/s.

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

Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 23.7 GB at Q4_K_M quantization. On Radeon PRO W7700 16GB, it fits at Q2_K using 17.1 GB.

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

The recommended quantization is Q4_K_M, but on Radeon PRO W7700 16GB the best fitting quantization is Q2_K, which uses 17.1 GB.

What speed will Nemotron Cascade 2 30B A3B run at on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Nemotron Cascade 2 30B A3B achieves approximately 45.4 tokens per second decode speed with a time-to-first-token of 4267ms using Q2_K quantization.

Can Radeon PRO W7700 16GB run Nemotron Cascade 2 30B A3B for coding?

For coding workloads, Nemotron Cascade 2 30B A3B on Radeon PRO W7700 16GB receives a F grade with 16.0 tok/s and 4K context.

What context window can Nemotron Cascade 2 30B A3B use on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Nemotron Cascade 2 30B A3B can safely use up to 10K tokens of context at Q2_K quantization. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron Cascade 2 30B A3B feels slow on Radeon PRO W7700 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for Radeon PRO W7700 16GB See all hardware for Nemotron Cascade 2 30B A3B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/nemotron-cascade-2-30b-a3b-on-radeon-pro-w7700-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: