Would CUDA be a better path than Intel Arc A730M 12GB for Nemotron Nano 8B?

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 Nano 8B run on Intel Arc A730M 12GB?

Q: What should I upgrade first if Nemotron Nano 8B feels slow on Intel Arc A730M 12GB?

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.

YES — Runs Great

S89Excellent

Estimated from fit model

Nemotron Nano 8B needs ~8.9 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 8.9 GB, 36.3 tok/s, Runs well

8.9 GB required12.0 GB available

74% VRAM used

Fit status

Runs well

Decode

36.3 tok/s

TTFT

5338 ms

Safe context

41K

Memory

8.9 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B 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: 36.3 tok/s decode · 5.3s TTFT (warm) · 91 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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	S	Runs well	36.3 tok/s	2912 ms	41K
Coding	S	Runs well	36.3 tok/s	5338 ms	41K
Agentic Coding	S	Tight fit	36.3 tok/s	7764 ms	41K
Reasoning	S	Runs well	36.3 tok/s	6309 ms	41K
RAG	S	Tight fit	36.3 tok/s	9706 ms	41K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A85
Q3_K_S	3	3.9 GB	Low	S86
NVFP4	4

Get started

Copy-paste commands to run Nemotron Nano 8B on your machine.

Run

lms load Llama-3.1-Nemotron-Nano-8B-v1 && lms server start

Your hardware

More models your Intel Arc A730M 12GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	32.2 tok/s
Qwen 3 14B	14B	A	13 tok/s

Frequently asked questions

Can Intel Arc A730M 12GB run Nemotron Nano 8B?

Yes, Intel Arc A730M 12GB can run Nemotron Nano 8B with a S grade (Runs well). Expected decode speed: 36.3 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 8B?

The recommended quantization for Nemotron Nano 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 8B run at on Intel Arc A730M 12GB?

On Intel Arc A730M 12GB, Nemotron Nano 8B achieves approximately 36.3 tokens per second decode speed with a time-to-first-token of 5338ms using Q4_K_M quantization.

Can Intel Arc A730M 12GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on Intel Arc A730M 12GB receives a S grade with 36.3 tok/s and 41K context.

What context window can Nemotron Nano 8B use on Intel Arc A730M 12GB?

On Intel Arc A730M 12GB, Nemotron Nano 8B can safely use up to 41K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron Nano 8B feels slow on Intel Arc A730M 12GB?

See all results for Intel Arc A730M 12GB See all hardware for Nemotron Nano 8B

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