Would CUDA be a better path than Intel Arc Pro B50 16GB for Qwen 2.5 Coder 7B?

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 7B run on Intel Arc Pro B50 16GB?

Q: What should I upgrade first if Qwen 2.5 Coder 7B feels slow on Intel Arc Pro B50 16GB?

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 — Runs Great

B68Good

Estimated from fit model

Qwen 2.5 Coder 7B needs ~7.6 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 7.6 GB, 30.8 tok/s, Runs well

7.6 GB required16.0 GB available

48% VRAM used

Fit status

Runs well

Decode

30.8 tok/s

TTFT

6295 ms

Safe context

131K

Memory

7.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 7B on Intel Arc Pro B50 16GB
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: 30.8 tok/s decode · 6.3s TTFT (warm) · 77 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	B	Runs well	30.8 tok/s	3433 ms	131K
Coding	B	Runs well	30.8 tok/s	6295 ms	131K
Agentic Coding	B	Runs well	30.8 tok/s	9156 ms	131K
Reasoning	B	Runs well	30.8 tok/s	7439 ms	131K
RAG	B	Runs well	30.8 tok/s	11445 ms	131K

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B67
Q3_K_S	3	3.4 GB	Low	B67
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 Coder 7B on your machine.

Run

ollama run qwen2.5-coder:7b

Upgrade options

Hardware that runs Qwen 2.5 Coder 7B well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+576)

Raises estimated decode speed by about 218%.98 tok/s decode

Raises estimated decode speed by about 218%.

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

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+736)

Raises estimated decode speed by about 218%.98 tok/s decode

Raises estimated decode speed by about 218%.

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

~$999 MSRP

Frequently asked questions

Can Intel Arc Pro B50 16GB run Qwen 2.5 Coder 7B?

Yes, Intel Arc Pro B50 16GB can run Qwen 2.5 Coder 7B with a B grade (Runs well). Expected decode speed: 30.8 tok/s.

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B (7B parameters) requires approximately 7.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 7B?

The recommended quantization for Qwen 2.5 Coder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 7B run at on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Qwen 2.5 Coder 7B achieves approximately 30.8 tokens per second decode speed with a time-to-first-token of 6295ms using Q4_K_M quantization.

Can Intel Arc Pro B50 16GB run Qwen 2.5 Coder 7B for coding?

For coding workloads, Qwen 2.5 Coder 7B on Intel Arc Pro B50 16GB receives a B grade with 30.8 tok/s and 131K context.

What context window can Qwen 2.5 Coder 7B use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Qwen 2.5 Coder 7B can safely use up to 131K 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 7B feels slow on Intel Arc Pro B50 16GB?

See all results for Intel Arc Pro B50 16GB See all hardware for Qwen 2.5 Coder 7B

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