Would CUDA be a better path than Intel Arc Pro B60 24GB for StarCoder2 3B?

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 StarCoder2 3B run on Intel Arc Pro B60 24GB?

Q: What should I upgrade first if StarCoder2 3B 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.

YES — Runs Great

C43Usable

Estimated from fit model

StarCoder2 3B needs ~5.6 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~42 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) — 5.6 GB, 42.0 tok/s, Runs well

5.6 GB required24.0 GB available

23% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

16K

Memory

5.6 GB / 24.0 GB

Memory breakdown

Weights1.8 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsStarCoder2 3B 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	42.0 tok/s	2514 ms	16K
Coding	C	Runs well	42.0 tok/s	4610 ms	16K
Agentic Coding	C	Runs well	42.0 tok/s	6705 ms	16K
Reasoning	C	Runs well	42.0 tok/s	5448 ms	16K
RAG	C	Runs well	42.0 tok/s	8381 ms	16K

Quantization options

How StarCoder2 3B (3B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	C42
Q3_K_S	3	1.5 GB	Low	C42
NVFP4	4

Get started

Copy-paste commands to run StarCoder2 3B on your machine.

Run

ollama run starcoder2:3b

Upgrade options

Hardware that runs StarCoder2 3B well

MacBook Pro M3 Pro 36GBBudget pick

36 GB Unified (+12)

This setup is broadly balanced for this model.42 tok/s decode

~$1,999 MSRP

RTX 5090 32GBBest value

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

Raises estimated decode speed by about 36%.57 tok/s decode

Raises estimated decode speed by about 36%.

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

Frequently asked questions

Can Intel Arc Pro B60 24GB run StarCoder2 3B?

Yes, Intel Arc Pro B60 24GB can run StarCoder2 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does StarCoder2 3B need?

StarCoder2 3B (3B parameters) requires approximately 5.6 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 3B?

The recommended quantization for StarCoder2 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 3B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, StarCoder2 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run StarCoder2 3B for coding?

For coding workloads, StarCoder2 3B on Intel Arc Pro B60 24GB receives a C grade with 42.0 tok/s and 16K context.

What context window can StarCoder2 3B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, StarCoder2 3B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 3B feels slow on Intel Arc Pro B60 24GB?

See all results for Intel Arc Pro B60 24GB See all hardware for StarCoder2 3B

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