Can Qwen 2.5 Coder 14B run on Intel Arc Pro B60 24GB?

YES — Runs Great

B66Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~14.8 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 14.8 GB, 31.1 tok/s, Runs well

14.8 GB required24.0 GB available

62% VRAM used

Fit status

Runs well

Decode

31.1 tok/s

TTFT

6217 ms

Safe context

66K

Memory

14.8 GB / 24.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on Intel Arc Pro B60 24GB
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: 31.1 tok/s decode · 6.2s TTFT (warm) · 78 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	31.1 tok/s	3391 ms	66K
Coding	B	Runs well	31.1 tok/s	6217 ms	66K
Agentic Coding	B	Runs well	31.1 tok/s	9043 ms	66K
Reasoning	B	Runs well	31.1 tok/s	7347 ms	66K
RAG	B	Runs well	31.1 tok/s	11304 ms	66K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B60
Q3_K_S	3	6.9 GB	Low	B61
NVFP4	4	7.8 GB	Medium	B61
Q4_K_M	4	8.5 GB	Medium	B62
Q5_K_M	5	10.1 GB	High	B63
Q6_K	6	11.5 GB	High	B64
Q8_0Best for your GPU	8	15.0 GB	Very High	B64
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

アップグレードオプション

Qwen 2.5 Coder 14Bを快適に動かすハードウェア

RTX 5090 32GBコスパ重視

32 GB VRAM (+8)1792 GB/s (+1336)

Raises estimated decode speed by about 374%.147.3 tok/sデコード

Raises estimated decode speed by about 374%.

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

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

〜$1,999 MSRP

RTX PRO 4500 Blackwell 32GBコスパ最良

32 GB VRAM (+8)896 GB/s (+440)

Raises estimated decode speed by about 206%.95.2 tok/sデコード

Raises estimated decode speed by about 206%.

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

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

〜$2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Qwen 2.5 Coder 14B?

Yes, Intel Arc Pro B60 24GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 31.1 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 14.8 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 B60 24GB?

On Intel Arc Pro B60 24GB, Qwen 2.5 Coder 14B achieves approximately 31.1 tokens per second decode speed with a time-to-first-token of 6217ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on Intel Arc Pro B60 24GB receives a B grade with 31.1 tok/s and 66K context.

What context window can Qwen 2.5 Coder 14B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen 2.5 Coder 14B can safely use up to 66K 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 B60 24GB?

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 B60 24GB 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 B60 24GB See all hardware for Qwen 2.5 Coder 14B

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