Can StarCoder2 15B run on Intel Arc Pro B60 24GB?

YES — Runs Great

C50Usable

Estimated from fit model

StarCoder2 15B needs ~14.2 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~27 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) — 14.2 GB, 26.9 tok/s, Runs well

14.2 GB required24.0 GB available

59% VRAM used

Fit status

Runs well

Decode

26.9 tok/s

TTFT

7194 ms

Safe context

105K

Memory

14.2 GB / 24.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsStarCoder2 15B 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: 26.9 tok/s decode · 7.2s TTFT (warm) · 67 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	C	Runs well	26.9 tok/s	3924 ms	105K
Coding	C	Runs well	26.9 tok/s	7194 ms	105K
Agentic Coding	C	Runs well	26.9 tok/s	10464 ms	105K
Reasoning	C	Runs well	26.9 tok/s	8502 ms	105K
RAG	C	Runs well	26.9 tok/s	13080 ms	105K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C46
Q3_K_S	3	7.4 GB	Low	C47
NVFP4	4	8.4 GB	Medium	C47
Q4_K_M	4	9.2 GB	Medium	C48
Q5_K_M	5	10.8 GB	High	C49
Q6_K	6	12.3 GB	High	C50
Q8_0Best for your GPU	8	16.1 GB	Very High	C50
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

lms load hf-second-state--starcoder2-15b-gguf && lms server start

Upgrade-Optionen

Hardware, die StarCoder2 15B gut ausführt

RTX 5090 32GBGünstige Wahl

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

Raises estimated decode speed by about 361%.124 tok/s Dekodierung

Raises estimated decode speed by about 361%.

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.

ca. $1,999 MSRP

RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis

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

Raises estimated decode speed by about 206%.82.3 tok/s Dekodierung

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.

ca. $2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run StarCoder2 15B?

Yes, Intel Arc Pro B60 24GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 26.9 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.2 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, StarCoder2 15B achieves approximately 26.9 tokens per second decode speed with a time-to-first-token of 7194ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on Intel Arc Pro B60 24GB receives a C grade with 26.9 tok/s and 105K context.

What context window can StarCoder2 15B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, StarCoder2 15B can safely use up to 105K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 15B 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 StarCoder2 15B?

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 StarCoder2 15B

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