Would CUDA be a better path than Intel Arc Pro B60 24GB 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 Pro B60 24GB?

Q: What should I upgrade first if Nemotron Nano 8B feels slow on Intel Arc Pro B60 24GB?

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

A84Great

Estimated from fit model

Nemotron Nano 8B needs ~10.1 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 10.1 GB, 54.2 tok/s, Runs well

10.1 GB required24.0 GB available

42% VRAM used

Fit status

Runs well

Decode

54.2 tok/s

TTFT

3569 ms

Safe context

130K

Memory

10.1 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on Intel Arc Pro B60 24GB
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: 54.2 tok/s decode · 3.6s TTFT (warm) · 136 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	A	Runs well	54.2 tok/s	1947 ms	130K
Coding	A	Runs well	54.2 tok/s	3569 ms	130K
Agentic Coding	S	Runs well	54.2 tok/s	5191 ms	130K
Reasoning	A	Runs well	54.2 tok/s	4218 ms	130K
RAG	S	Runs well	54.2 tok/s	6489 ms	130K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A80
Q3_K_S	3	3.9 GB	Low	A80
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 Pro B60 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	37.2 tok/s
Qwen 3.5 27B	27B	S	16.1 tok/s

Frequently asked questions

Can Intel Arc Pro B60 24GB run Nemotron Nano 8B?

Yes, Intel Arc Pro B60 24GB can run Nemotron Nano 8B with a A grade (Runs well). Expected decode speed: 54.2 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 10.1 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 Pro B60 24GB?

On Intel Arc Pro B60 24GB, Nemotron Nano 8B achieves approximately 54.2 tokens per second decode speed with a time-to-first-token of 3569ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on Intel Arc Pro B60 24GB receives a A grade with 54.2 tok/s and 130K context.

What context window can Nemotron Nano 8B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Nemotron Nano 8B can safely use up to 130K 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 Pro B60 24GB?

See all results for Intel Arc Pro B60 24GB See all hardware for Nemotron Nano 8B

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