Would CUDA be a better path than Intel Arc A370M 4GB for Qwen 3.5 4B?

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 Qwen 3.5 4B run on Intel Arc A370M 4GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen 3.5 4B needs ~5.9 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) — 5.9 GB, exceeds 4.0 GB available

5.9 GB required4.0 GB available

148% VRAM needed

1.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.9 tok/s

TTFT

24517 ms

Safe context

Memory

5.9 GB / 4.0 GB

Offload

30%

Memory breakdown

Weights2.4 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3.5 4B 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: 7.9 tok/s decode · 24.5s TTFT (warm) · 20 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 5.9 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	12.1 tok/s	8693 ms	4K
Coding	F	Too heavy	7.9 tok/s	24517 ms	4K
Agentic Coding	F	Too heavy	4.1 tok/s	69191 ms	4K
Reasoning	F	Too heavy	7.9 tok/s	28975 ms	4K
RAG	F	Too heavy	4.1 tok/s	86489 ms	4K

Quantization options

How Qwen 3.5 4B (4B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	1.6 GB	Low	S95
Q3_K_S	3	2.0 GB	Low	F0

Upgrade options

Hardware that runs Qwen 3.5 4B well

Intel Arc A380 6GBBudget pick

6 GB VRAM (+2)186 GB/s (+74)

Makes the model fit on the accelerator instead of staying completely out of reach.28.7 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.

~$139 MSRP

Intel Arc A580 8GBBest value

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

Makes the model fit on the accelerator instead of staying completely out of reach.56 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.

~$179 MSRP

Intel Arc B570 10GBIntel upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.56 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

Frequently asked questions

Can Intel Arc A370M 4GB run Qwen 3.5 4B?

No, Qwen 3.5 4B requires more memory than Intel Arc A370M 4GB provides.

How much VRAM does Qwen 3.5 4B need?

Qwen 3.5 4B (4B parameters) requires approximately 5.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 4B?

The recommended quantization for Qwen 3.5 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 4B run at on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, Qwen 3.5 4B achieves approximately 7.9 tokens per second decode speed with a time-to-first-token of 24517ms using Q4_K_M quantization.

Can Intel Arc A370M 4GB run Qwen 3.5 4B for coding?

For coding workloads, Qwen 3.5 4B on Intel Arc A370M 4GB receives a F grade with 7.9 tok/s and 4K context.

What context window can Qwen 3.5 4B use on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, Qwen 3.5 4B 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 Qwen 3.5 4B 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.

See all results for Intel Arc A370M 4GB See all hardware for Qwen 3.5 4B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-3.5-4b-on-arc-a370m-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: