Can Qwen 2.5 Coder 14B run on Intel Arc Pro B50 16GB?

YES — Tight Fit

B62Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~14.0 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~15 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) — 14.0 GB, 15.3 tok/s, Tight fit

14.0 GB required16.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

15.3 tok/s

TTFT

12656 ms

Safe context

27K

Memory

14.0 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B 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: 15.3 tok/s decode · 12.7s TTFT (warm) · 38 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	15.3 tok/s	6903 ms	27K
Coding	B	Tight fit	15.3 tok/s	12656 ms	27K
Agentic Coding	C	Runs with offload (needs ~0.5 GB host RAM)	10.5 tok/s	26914 ms	27K
Reasoning	B	Tight fit	15.3 tok/s	14957 ms	27K
RAG	C	Runs with offload (needs ~0.5 GB host RAM)	10.5 tok/s	33643 ms	27K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B64
Q3_K_S	3	6.9 GB	Low	B65
NVFP4	4	7.8 GB	Medium	B66
Q4_K_M	4	8.5 GB	Medium	B66
Q5_K_M	5	10.1 GB	High	B65
Q6_KBest for your GPU	6	11.5 GB	High	B65
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

Get started

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

Run

ollama run qwen2.5-coder:14b

Opções de upgrade

Hardware que roda bem Qwen 2.5 Coder 14B

Intel Arc Pro B60 24GBOpção econômica

24 GB VRAM (+8)456 GB/s (+232)

Raises estimated decode speed by about 103%.31.1 tok/s decodificação

Raises estimated decode speed by about 103%.

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

~$599 MSRP

MacBook Pro M4 32GBMelhor custo-benefício

32 GB Unified (+16)

Adds memory headroom for longer context windows and future model growth.9.6 tok/s decodificação

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

~$799 MSRP

Frequently asked questions

Can Intel Arc Pro B50 16GB run Qwen 2.5 Coder 14B?

Yes, Intel Arc Pro B50 16GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 15.3 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 14.0 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 B50 16GB?

On Intel Arc Pro B50 16GB, Qwen 2.5 Coder 14B achieves approximately 15.3 tokens per second decode speed with a time-to-first-token of 12656ms using Q4_K_M quantization.

Can Intel Arc Pro B50 16GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on Intel Arc Pro B50 16GB receives a B grade with 15.3 tok/s and 27K context.

What context window can Qwen 2.5 Coder 14B use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Qwen 2.5 Coder 14B can safely use up to 27K 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 14B 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.

Would CUDA be a better path than Intel Arc Pro B50 16GB 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.

See all results for Intel Arc Pro B50 16GB See all hardware for Qwen 2.5 Coder 14B

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