Can Nemotron Cascade 2 30B A3B run on RTX 5070 12GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~23.6 GB but RTX 5070 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity

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) — 23.6 GB, exceeds 12.0 GB available

23.6 GB required12.0 GB available

197% VRAM needed

11.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.6 tok/s

TTFT

15412 ms

Safe context

Memory

23.6 GB / 12.0 GB

Offload

50%

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom1.2 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 5070 12GB
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: 12.6 tok/s decode · 15.4s TTFT (warm) · 31 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 23.6 GB, but this setup only exposes 12.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	14.3 tok/s	7378 ms	4K
Coding	F	Too heavy	12.6 tok/s	15412 ms	4K
Agentic Coding	F	Too heavy	9.9 tok/s	28453 ms	4K
Reasoning	F	Too heavy	12.6 tok/s	18214 ms	4K
RAG	F	Too heavy	9.9 tok/s	35566 ms	4K

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on RTX 5070 12GB (12.0 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

Upgrade options

Hardware that runs Nemotron Cascade 2 30B A3B well

RTX 3090 24GBBudget pick

24 GB VRAM (+12)936 GB/s (+264)

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

RTX 4090 24GBBest value

24 GB VRAM (+12)1008 GB/s (+336)

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

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+12)

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

Can RTX 5070 12GB run Nemotron Cascade 2 30B A3B?

No, Nemotron Cascade 2 30B A3B requires more memory than RTX 5070 12GB provides.

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

Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 23.6 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 RTX 5070 12GB?

On RTX 5070 12GB, Nemotron Cascade 2 30B A3B achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15412ms using Q4_K_M quantization.

Can RTX 5070 12GB run Nemotron Cascade 2 30B A3B for coding?

For coding workloads, Nemotron Cascade 2 30B A3B on RTX 5070 12GB receives a F grade with 12.6 tok/s and 4K context.

What context window can Nemotron Cascade 2 30B A3B use on RTX 5070 12GB?

On RTX 5070 12GB, Nemotron Cascade 2 30B A3B can safely use up to 4K tokens of context. 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 RTX 5070 12GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 5070 12GB 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-rtx-5070-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

14.3 tok/s

7378 ms

Coding

Too heavy

12.6 tok/s

15412 ms

Agentic Coding

Too heavy

9.9 tok/s

28453 ms

Reasoning

Too heavy

12.6 tok/s

18214 ms

RAG

Too heavy

9.9 tok/s

35566 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

11.7 GB

Low

Q3_K_S

14.7 GB

Low

NVFP4

16.8 GB

Medium

Q4_K_M

18.3 GB

Medium

Q5_K_M

21.6 GB

High

Q6_K

24.6 GB

High

Q8_0

32.1 GB

Very High

F16

61.5 GB

Maximum