Would CUDA be a better path than Intel Arc B580 12GB for StarCoder2 7B?

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 7B run on Intel Arc B580 12GB?

Q: What should I upgrade first if StarCoder2 7B feels slow on Intel Arc B580 12GB?

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

C52Usable

Estimated from fit model

StarCoder2 7B needs ~6.9 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~51 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) — 6.9 GB, 56.0 tok/s, Runs well

6.9 GB required12.0 GB available

58% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3460 ms

Safe context

16K

Memory

6.9 GB / 12.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on Intel Arc B580 12GB
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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 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	51.3 tok/s	2060 ms	16K
Coding	C	Runs well	51.3 tok/s	3777 ms	16K
Agentic Coding	C	Runs well	51.3 tok/s	5494 ms	16K
Reasoning	C	Runs well	51.3 tok/s	4464 ms	16K
RAG	C	Runs well	51.3 tok/s	6867 ms	16K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C48
Q3_K_S	3	3.4 GB	Low	C49
NVFP4	4

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load starcoder2-7b && lms server start

Frequently asked questions

Can Intel Arc B580 12GB run StarCoder2 7B?

Yes, Intel Arc B580 12GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 51.3 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 6.9 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on Intel Arc B580 12GB?

On Intel Arc B580 12GB, StarCoder2 7B achieves approximately 51.3 tokens per second decode speed with a time-to-first-token of 3777ms using Q4_K_M quantization.

Can Intel Arc B580 12GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on Intel Arc B580 12GB receives a C grade with 51.3 tok/s and 16K context.

What context window can StarCoder2 7B use on Intel Arc B580 12GB?

On Intel Arc B580 12GB, StarCoder2 7B 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 7B feels slow on Intel Arc B580 12GB?

See all results for Intel Arc B580 12GB See all hardware for StarCoder2 7B

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