Would CUDA be a better path than Intel Arc B570 10GB for Nemotron Nano 9B v2?

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 9B v2 run on Intel Arc B570 10GB?

Q: What should I upgrade first if Nemotron Nano 9B v2 feels slow on Intel Arc B570 10GB?

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 — With Offload

A81Great

Estimated from fit model

Nemotron Nano 9B v2 needs ~9.8 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 9.8 GB, 40.2 tok/s, Runs with offload

9.8 GB required10.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

40.2 tok/s

TTFT

4818 ms

Safe context

17K

Memory

9.8 GB / 10.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on Intel Arc B570 10GB
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: 40.2 tok/s decode · 4.8s TTFT (warm) · 101 tok/s prefill

What limits this setup

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

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.

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.

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	40.2 tok/s	2628 ms	17K
Coding	A	Runs with offload	40.2 tok/s	4818 ms	17K
Agentic Coding	F	Too heavy	20.2 tok/s	13918 ms	17K
Reasoning	A	Runs with offload	40.2 tok/s	5694 ms	17K
RAG	F	Too heavy	20.2 tok/s	17397 ms	17K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A81
Q3_K_S	3	4.4 GB	Low	A82
NVFP4	4

Get started

Copy-paste commands to run Nemotron Nano 9B v2 on your machine.

Run

ollama run nemotron-nano:9b-v2

Frequently asked questions

Can Intel Arc B570 10GB run Nemotron Nano 9B v2?

Yes, Intel Arc B570 10GB can run Nemotron Nano 9B v2 with a A grade (Runs with offload). Expected decode speed: 40.2 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 9.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 9B v2?

The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 9B v2 run at on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Nemotron Nano 9B v2 achieves approximately 40.2 tokens per second decode speed with a time-to-first-token of 4818ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on Intel Arc B570 10GB receives a A grade with 40.2 tok/s and 17K context.

What context window can Nemotron Nano 9B v2 use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Nemotron Nano 9B v2 can safely use up to 17K 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 9B v2 feels slow on Intel Arc B570 10GB?

See all results for Intel Arc B570 10GB See all hardware for Nemotron Nano 9B v2

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