Would CUDA be a better path than Intel Arc Pro A40 6GB for Qwen 2.5 Coder 3B?

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 3B run on Intel Arc Pro A40 6GB?

Q: What should I upgrade first if Qwen 2.5 Coder 3B feels slow on Intel Arc Pro A40 6GB?

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.

YES — Tight Fit

A77Great

Estimated from fit model

Qwen 2.5 Coder 3B needs ~5.5 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very 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) — 5.5 GB, 42.0 tok/s, Tight fit

5.5 GB required6.0 GB available

92% VRAM used

Fit status

Tight fit

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

19K

Memory

5.5 GB / 6.0 GB

Memory breakdown

Weights1.8 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 3B 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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	A	Runs well	42.0 tok/s	2514 ms	19K
Coding	A	Tight fit	42.0 tok/s	4610 ms	19K
Agentic Coding	F	Too heavy	23.8 tok/s	11839 ms	19K
Reasoning	A	Tight fit	42.0 tok/s	5448 ms	19K
RAG	F	Too heavy	23.8 tok/s	14798 ms	19K

Quantization options

How Qwen 2.5 Coder 3B (3B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	A79
Q3_K_S	3	1.5 GB	Low	A80
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 Coder 3B on your machine.

Run

ollama run qwen2.5-coder:3b

Your hardware

More models your Intel Arc Pro A40 6GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 4B	4B	S	29.6 tok/s
Phi-4 Mini Reasoning 4B	3.8B	S	43.6 tok/s

Frequently asked questions

Can Intel Arc Pro A40 6GB run Qwen 2.5 Coder 3B?

Yes, Intel Arc Pro A40 6GB can run Qwen 2.5 Coder 3B with a A grade (Tight fit). Expected decode speed: 42.0 tok/s.

How much VRAM does Qwen 2.5 Coder 3B need?

Qwen 2.5 Coder 3B (3B parameters) requires approximately 5.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 3B?

The recommended quantization for Qwen 2.5 Coder 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 3B run at on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, Qwen 2.5 Coder 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can Intel Arc Pro A40 6GB run Qwen 2.5 Coder 3B for coding?

For coding workloads, Qwen 2.5 Coder 3B on Intel Arc Pro A40 6GB receives a A grade with 42.0 tok/s and 19K context.

What context window can Qwen 2.5 Coder 3B use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, Qwen 2.5 Coder 3B can safely use up to 19K 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 2.5 Coder 3B feels slow on Intel Arc Pro A40 6GB?

See all results for Intel Arc Pro A40 6GB See all hardware for Qwen 2.5 Coder 3B

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