Would CUDA be a better path than Intel Arc Pro A40 6GB 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 A40 6GB?

Q: What should I upgrade first if Nemotron Nano 8B feels slow on Intel Arc Pro A40 6GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_K

A74Great

Estimated from fit model

Nemotron Nano 8B needs ~6.6 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q2_K quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

Nemotron Nano 8B at Q4_K_M needs 8.3 GB — too much for Intel Arc Pro A40 6GB (6.0 GB). Runs at Q2_K (6.6 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.3 GB, exceeds 6.0 GB available

8.3 GB required6.0 GB available

138% VRAM needed

2.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.8 tok/s

TTFT

24869 ms

Safe context

Memory

8.3 GB / 6.0 GB

Offload

30%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron Nano 8B on Intel Arc Pro A40 6GB
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: 7.8 tok/s decode · 24.9s TTFT (warm) · 20 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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	F	Too heavy	10.1 tok/s	10434 ms	4K
Coding	F	Too heavy	7.8 tok/s	24869 ms	4K
Agentic Coding	F	Too heavy	5.0 tok/s	56348 ms	4K
Reasoning	F	Too heavy	7.8 tok/s	29390 ms	4K
RAG	F	Too heavy	5.0 tok/s	70435 ms	4K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.1 GB	Low	S89
Q3_K_S	3	3.9 GB	Low	F0

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

Upgrade options

Hardware that runs Nemotron Nano 8B well

Intel Arc A580 8GBBudget pick

8 GB VRAM (+2)512 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.36.2 tok/s decode

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

Raises estimated decode speed by about 364%.

~$179 MSRP

Intel Arc B570 10GBBest value

10 GB VRAM (+4)380 GB/s (+188)

Makes the model fit on the accelerator instead of staying completely out of reach.45.2 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.

~$219 MSRP

Intel Arc B580 12GBIntel upgrade

12 GB VRAM (+6)456 GB/s (+264)

Makes the model fit on the accelerator instead of staying completely out of reach.48.2 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.

~$249 MSRP

Frequently asked questions

Can Intel Arc Pro A40 6GB run Nemotron Nano 8B?

Yes, Intel Arc Pro A40 6GB can run Nemotron Nano 8B at Q2_K quantization (Very compromised (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 8.3 GB which exceeds available memory, but at Q2_K it needs only 6.6 GB. Expected decode speed: 17.1 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 8.3 GB at Q4_K_M quantization. On Intel Arc Pro A40 6GB, it fits at Q2_K using 6.6 GB.

What is the best quantization for Nemotron Nano 8B?

The recommended quantization is Q4_K_M, but on Intel Arc Pro A40 6GB the best fitting quantization is Q2_K, which uses 6.6 GB.

What speed will Nemotron Nano 8B run at on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, Nemotron Nano 8B achieves approximately 17.1 tokens per second decode speed with a time-to-first-token of 11352ms using Q2_K quantization.

Can Intel Arc Pro A40 6GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on Intel Arc Pro A40 6GB receives a F grade with 7.8 tok/s and 4K context.

What context window can Nemotron Nano 8B use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, Nemotron Nano 8B can safely use up to 11K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Intel Arc Pro A40 6GB See all hardware for Nemotron Nano 8B

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