Would CUDA be a better path than Intel Arc A730M 12GB for Nemotron Cascade 2 30B A3B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can Nemotron Cascade 2 30B A3B run on Intel Arc A730M 12GB?

Q: What should I upgrade first if Nemotron Cascade 2 30B A3B feels slow on Intel Arc A730M 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.

NO — Won't Fit

F0Won't run

Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~23.3 GB but Intel Arc A730M 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: 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) — 23.3 GB, exceeds 12.0 GB available

23.3 GB required12.0 GB available

194% VRAM needed

11.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.7 tok/s

TTFT

41106 ms

Safe context

Memory

23.3 GB / 12.0 GB

Offload

50%

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime0.9 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 Intel Arc A730M 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: 4.7 tok/s decode · 41.1s TTFT (warm) · 12 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 23.3 GB, but this setup only exposes 12.0 GB of usable VRAM.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

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.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	5.4 tok/s	19561 ms	4K
Coding	F	Too heavy	4.7 tok/s	41106 ms	4K
Agentic Coding	F	Too heavy	3.8 tok/s	73762 ms	4K
Reasoning	F	Too heavy	4.7 tok/s	48580 ms	4K
RAG	F	Too heavy	3.8 tok/s	92202 ms	4K

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on Intel Arc A730M 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

Upgrade options

Hardware that runs Nemotron Cascade 2 30B A3B well

Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+12)456 GB/s (+120)

Makes the model fit on the accelerator instead of staying completely out of reach.27.8 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.

~$599 MSRP

RTX 5090 32GBBiggest leap

32 GB VRAM (+20)1792 GB/s (+1456)

Makes the model fit on the accelerator instead of staying completely out of reach.185.6 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.

~$1,999 MSRP

Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+116)3200 GB/s (+2864)

Makes the model fit on the accelerator instead of staying completely out of reach.311.6 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.

~$15,000 MSRP

Gaudi 3 128GBIntel upgrade

128 GB VRAM (+116)3700 GB/s (+3364)

Makes the model fit on the accelerator instead of staying completely out of reach.400.4 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.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc A730M 12GB run Nemotron Cascade 2 30B A3B?

No, Nemotron Cascade 2 30B A3B requires more memory than Intel Arc A730M 12GB provides.

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

Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 23.3 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 Intel Arc A730M 12GB?

On Intel Arc A730M 12GB, Nemotron Cascade 2 30B A3B achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41106ms using Q4_K_M quantization.

Can Intel Arc A730M 12GB run Nemotron Cascade 2 30B A3B for coding?

For coding workloads, Nemotron Cascade 2 30B A3B on Intel Arc A730M 12GB receives a F grade with 4.7 tok/s and 4K context.

What context window can Nemotron Cascade 2 30B A3B use on Intel Arc A730M 12GB?

On Intel Arc A730M 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.

See all results for Intel Arc A730M 12GB See all hardware for Nemotron Cascade 2 30B A3B

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