Would CUDA be a better path than Intel Arc Pro A60 12GB for Qwen 2.5 Coder 14B?

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 14B run on Intel Arc Pro A60 12GB?

Q: What should I upgrade first if Qwen 2.5 Coder 14B feels slow on Intel Arc Pro A60 12GB?

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.

BARELY — Tight on Memory

C52Usable

Estimated from fit model

Qwen 2.5 Coder 14B needs ~13.6 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.6 GB, 13.8 tok/s, Very compromised (needs ~1 GB host RAM)

13.6 GB required12.0 GB available

113% VRAM needed

1.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

13.8 tok/s

TTFT

14052 ms

Safe context

Memory

13.6 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B 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: 13.8 tok/s decode · 14.1s TTFT (warm) · 34 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	B	Runs with offload (needs ~0.1 GB host RAM)	17.5 tok/s	6026 ms	7K
Coding	C	Very compromised	12.8 tok/s	15176 ms	7K
Agentic Coding	F	Too heavy	9.1 tok/s	30844 ms	7K
Reasoning	C	Very compromised (needs ~1 GB host RAM)	13.8 tok/s	16607 ms	7K
RAG	F	Too heavy	9.1 tok/s	38555 ms

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B67
Q3_K_S	3	6.9 GB	Low	B66
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.

Run

ollama run qwen2.5-coder:14b

Upgrade options

Hardware that runs Qwen 2.5 Coder 14B well

Intel Arc A770 16GBBudget pick

16 GB VRAM (+4)560 GB/s (+176)

Removes host-memory offload, which is usually the single biggest latency and throughput win.31.9 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 131%.

~$349 MSRP

Intel Arc Pro B50 16GBBest value

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.15.3 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$399 MSRP

Intel Arc Pro B60 24GBIntel upgrade

24 GB VRAM (+12)456 GB/s (+72)

Removes host-memory offload, which is usually the single biggest latency and throughput win.31.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 125%.

~$599 MSRP

Frequently asked questions

Can Intel Arc Pro A60 12GB run Qwen 2.5 Coder 14B?

Yes, Intel Arc Pro A60 12GB can run Qwen 2.5 Coder 14B with a C grade (Very compromised). Expected decode speed: 12.8 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 13.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 14B?

The recommended quantization for Qwen 2.5 Coder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 14B run at on Intel Arc Pro A60 12GB?

On Intel Arc Pro A60 12GB, Qwen 2.5 Coder 14B achieves approximately 12.8 tokens per second decode speed with a time-to-first-token of 15176ms using Q4_K_M quantization.

Can Intel Arc Pro A60 12GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on Intel Arc Pro A60 12GB receives a C grade with 12.8 tok/s and 7K context.

What context window can Qwen 2.5 Coder 14B use on Intel Arc Pro A60 12GB?

On Intel Arc Pro A60 12GB, Qwen 2.5 Coder 14B can safely use up to 7K 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 14B

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