Would CUDA be a better path than Intel Arc Pro A60 12GB for Qwen 2.5 Coder 0.5B?

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 2.5 Coder 0.5B run on Intel Arc Pro A60 12GB?

Q: What should I upgrade first if Qwen 2.5 Coder 0.5B feels slow on Intel Arc Pro A60 12GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Runs Great

C47Usable

Estimated from fit model

Qwen 2.5 Coder 0.5B needs ~2.6 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Memory bandwidth

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) — 2.6 GB, 7.0 tok/s, Runs well

2.6 GB required12.0 GB available

22% VRAM used

Fit status

Runs well

Decode

7.0 tok/s

TTFT

27657 ms

Safe context

131K

Memory

2.6 GB / 12.0 GB

Memory breakdown

Weights0.3 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 0.5B on Intel Arc Pro A60 12GB
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: 7.0 tok/s decode · 27.7s TTFT (warm) · 18 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 7.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

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	C	Runs well	7.0 tok/s	15086 ms	131K
Coding	C	Runs well	7.0 tok/s	27657 ms	131K
Agentic Coding	C	Runs well	7.0 tok/s	40229 ms	131K
Reasoning	C	Runs well	7.0 tok/s	32686 ms	131K
RAG	C	Runs well	7.0 tok/s	50286 ms	131K

Quantization options

How Qwen 2.5 Coder 0.5B (0.5B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.2 GB	Low	B55
Q3_K_S	3	0.2 GB	Low	B55
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 Coder 0.5B on your machine.

Run

ollama run qwen2.5-coder:0.5b

Upgrade options

Hardware that runs Qwen 2.5 Coder 0.5B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+4)448 GB/s (+64)

Raises estimated decode speed by about 36%.9.5 tok/s decode

Raises estimated decode speed by about 36%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RTX 5070 Ti 16GBBest value

16 GB VRAM (+4)896 GB/s (+512)

Raises estimated decode speed by about 36%.9.5 tok/s decode

Raises estimated decode speed by about 36%.

Adds memory headroom for longer context windows and future model growth.

~$749 MSRP

Frequently asked questions

Can Intel Arc Pro A60 12GB run Qwen 2.5 Coder 0.5B?

Yes, Intel Arc Pro A60 12GB can run Qwen 2.5 Coder 0.5B with a C grade (Runs well). Expected decode speed: 7.0 tok/s.

How much VRAM does Qwen 2.5 Coder 0.5B need?

Qwen 2.5 Coder 0.5B (0.5B parameters) requires approximately 2.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 0.5B?

The recommended quantization for Qwen 2.5 Coder 0.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 0.5B run at on Intel Arc Pro A60 12GB?

On Intel Arc Pro A60 12GB, Qwen 2.5 Coder 0.5B achieves approximately 7.0 tokens per second decode speed with a time-to-first-token of 27657ms using Q4_K_M quantization.

Can Intel Arc Pro A60 12GB run Qwen 2.5 Coder 0.5B for coding?

For coding workloads, Qwen 2.5 Coder 0.5B on Intel Arc Pro A60 12GB receives a C grade with 7.0 tok/s and 131K context.

What context window can Qwen 2.5 Coder 0.5B use on Intel Arc Pro A60 12GB?

On Intel Arc Pro A60 12GB, Qwen 2.5 Coder 0.5B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Intel Arc Pro A60 12GB See all hardware for Qwen 2.5 Coder 0.5B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-0.5b-on-arc-pro-a60-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: