Would CUDA be a better path than Intel Arc B570 10GB 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 B570 10GB?

Q: What should I upgrade first if Nemotron Nano 8B 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 — Tight Fit

S87Excellent

Estimated from fit model

Nemotron Nano 8B needs ~8.7 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 8.7 GB, 45.2 tok/s, Tight fit

8.7 GB required10.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

45.2 tok/s

TTFT

4283 ms

Safe context

26K

Memory

8.7 GB / 10.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B 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: 45.2 tok/s decode · 4.3s TTFT (warm) · 113 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	45.2 tok/s	2336 ms	26K
Coding	S	Tight fit	45.2 tok/s	4283 ms	26K
Agentic Coding	A	Runs with offload (needs ~0.3 GB host RAM)	30.2 tok/s	9328 ms	26K
Reasoning	S	Tight fit	45.2 tok/s	5062 ms	26K
RAG	A	Runs with offload (needs ~0.3 GB host RAM)	30.2 tok/s	11660 ms

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	S86
Q3_K_S	3	3.9 GB	Low	S88
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 B570 10GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	40.2 tok/s

Frequently asked questions

Can Intel Arc B570 10GB run Nemotron Nano 8B?

Yes, Intel Arc B570 10GB can run Nemotron Nano 8B with a S grade (Tight fit). Expected decode speed: 45.2 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 8.7 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 B570 10GB?

On Intel Arc B570 10GB, Nemotron Nano 8B achieves approximately 45.2 tokens per second decode speed with a time-to-first-token of 4283ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on Intel Arc B570 10GB receives a S grade with 45.2 tok/s and 26K context.

What context window can Nemotron Nano 8B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Nemotron Nano 8B can safely use up to 26K 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 B570 10GB?

See all results for Intel Arc B570 10GB See all hardware for Nemotron Nano 8B

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