Would CUDA be a better path than Intel Arc Pro B50 16GB for Qwen 2.5 Coder 1.5B?

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 1.5B run on Intel Arc Pro B50 16GB?

Q: What should I upgrade first if Qwen 2.5 Coder 1.5B 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

B61Good

Estimated from fit model

Qwen 2.5 Coder 1.5B needs ~3.8 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~21 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) — 3.8 GB, 21.0 tok/s, Runs well

3.8 GB required16.0 GB available

24% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

33K

Memory

3.8 GB / 16.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.4 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 1.5B 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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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	21.0 tok/s	5029 ms	33K
Coding	B	Runs well	21.0 tok/s	9219 ms	33K
Agentic Coding	B	Runs well	21.0 tok/s	13410 ms	33K
Reasoning	B	Runs well	21.0 tok/s	10895 ms	33K
RAG	B	Runs well	21.0 tok/s	16762 ms	33K

Quantization options

How Qwen 2.5 Coder 1.5B (1.5B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	B64
Q3_K_S	3	0.7 GB	Low	B64
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 Coder 1.5B on your machine.

Run

ollama run qwen2.5-coder:1.5b

Upgrade options

Hardware that runs Qwen 2.5 Coder 1.5B well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Adds memory headroom for longer context windows and future model growth.21 tok/s decode

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

~$799 MSRP

MacBook Pro M3 24GBBest value

24 GB Unified (+8)

This setup is broadly balanced for this model.21 tok/s decode

~$1,099 MSRP

Frequently asked questions

Can Intel Arc Pro B50 16GB run Qwen 2.5 Coder 1.5B?

Yes, Intel Arc Pro B50 16GB can run Qwen 2.5 Coder 1.5B with a B grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Qwen 2.5 Coder 1.5B need?

Qwen 2.5 Coder 1.5B (1.5B parameters) requires approximately 3.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 1.5B?

The recommended quantization for Qwen 2.5 Coder 1.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 1.5B run at on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Qwen 2.5 Coder 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can Intel Arc Pro B50 16GB run Qwen 2.5 Coder 1.5B for coding?

For coding workloads, Qwen 2.5 Coder 1.5B on Intel Arc Pro B50 16GB receives a B grade with 21.0 tok/s and 33K context.

What context window can Qwen 2.5 Coder 1.5B use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Qwen 2.5 Coder 1.5B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for Intel Arc Pro B50 16GB See all hardware for Qwen 2.5 Coder 1.5B

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