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URL: https://willitrunai.com/can-run/nemotron-cascade-2-30b-a3b-on-rtx-4000-ada-20gb

⇱ Nemotron Cascade 2 30B A3B on RTX 4000 Ada 20GB? No — Alter…

Can Nemotron Cascade 2 30B A3B run on RTX 4000 Ada 20GB?

YES — With NVFP4

A78Great
○Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~22.9 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With NVFP4 quantization, expect ~28 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload
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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 24.4 GB — too much for RTX 4000 Ada 20GB (20.0 GB). Runs at NVFP4 (22.9 GB) with medium quality. 3 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 24.4 GB, exceeds 20.0 GB available
24.4 GB required20.0 GB available
122% VRAM needed

4.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

21.4 tok/s

TTFT

9061 ms

Safe context

4K

Memory

24.4 GB / 20.0 GB

Offload

20%

Memory breakdown

Weights18.3 GB
KV Cache2.9 GB
Runtime1.2 GB
Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsNemotron Cascade 2 30B A3B on RTX 4000 Ada 20GB
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: 21.4 tok/s decode · 9.1s TTFT (warm) · 53 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 2.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatAVery compromised (needs ~2.4 GB host RAM)24.3 tok/s4339 ms4K
CodingFToo heavy21.4 tok/s9061 ms4K
Agentic CodingFToo heavy16.8 tok/s16728 ms4K
ReasoningFToo heavy21.4 tok/s10708 ms4K
RAGFToo heavy16.8 tok/s20910 ms4K

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.7 GB
LowS88
Q3_K_SBest for your GPU
3
14.7 GB
LowS88
NVFP4
4
16.8 GB
MediumF0
Q4_K_M
4
18.3 GB
MediumF0
Q5_K_M
5
21.6 GB
HighF0
Q6_K
6
24.6 GB
HighF0
Q8_0
8
32.1 GB
Very HighF0
F16
16
61.5 GB
MaximumF0

Get started

Copy-paste commands to run Nemotron Cascade 2 30B A3B on your machine.

Run

ollama run nemotron-cascade-2

Upgrade options

Hardware that runs Nemotron Cascade 2 30B A3B well

👁 NVIDIA
RTX 3090 24GBBudget pick
24 GB VRAM (+4)936 GB/s (+576)
S
Makes the model fit on the accelerator instead of staying completely out of reach.70.7 tok/s decode

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.

~$1,499 MSRP

👁 NVIDIA
RTX 4090 24GBBest value
24 GB VRAM (+4)1008 GB/s (+648)
S
Makes the model fit on the accelerator instead of staying completely out of reach.82.7 tok/s decode

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.

~$1,599 MSRP

👁 NVIDIA
RTX PRO 4000 Blackwell 24GBNVIDIA upgrade
24 GB VRAM (+4)672 GB/s (+312)
S
Makes the model fit on the accelerator instead of staying completely out of reach.62.3 tok/s decode

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.

~$1,599 MSRP

Frequently asked questions

See all results for RTX 4000 Ada 20GBSee all hardware for Nemotron Cascade 2 30B A3B