Would CUDA be a better path than Intel Arc A370M 4GB 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 A370M 4GB?

Q: What should I upgrade first if Nemotron Nano 8B feels slow on Intel Arc A370M 4GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

Nemotron Nano 8B needs ~8.1 GB but Intel Arc A370M 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 8.1 GB, exceeds 4.0 GB available

8.1 GB required4.0 GB available

203% VRAM needed

4.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

95104 ms

Safe context

Memory

8.1 GB / 4.0 GB

Offload

50%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron Nano 8B on Intel Arc A370M 4GB
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: 2.0 tok/s decode · 95.1s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 8.1 GB, but this setup only exposes 4.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

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	F	Too heavy	2.7 tok/s	39629 ms	4K
Coding	F	Too heavy	2.0 tok/s	95104 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	112395 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	F0
Q3_K_S	3	3.9 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Nemotron Nano 8B well

Intel Arc A580 8GBBudget pick

8 GB VRAM (+4)512 GB/s (+400)

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 1710%.

~$179 MSRP

Intel Arc B570 10GBBest value

10 GB VRAM (+6)380 GB/s (+268)

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 (+8)456 GB/s (+344)

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 A370M 4GB run Nemotron Nano 8B?

No, Nemotron Nano 8B requires more memory than Intel Arc A370M 4GB provides.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 8.1 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 A370M 4GB?

On Intel Arc A370M 4GB, Nemotron Nano 8B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 95104ms using Q4_K_M quantization.

Can Intel Arc A370M 4GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on Intel Arc A370M 4GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Nemotron Nano 8B use on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, Nemotron Nano 8B can safely use up to 4K 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 A370M 4GB?

See all results for Intel Arc A370M 4GB See all hardware for Nemotron Nano 8B

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