Can StarCoder2 15B run on Intel Arc B580 12GB?

BARELY — Tight on Memory

D33Poor

○Estimated from fit model

StarCoder2 15B needs ~14.1 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q5_K_M quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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

Q5_K_M (High quality) — 14.1 GB, 12.4 tok/s, Very compromised (needs ~1.6 GB host RAM)

14.1 GB required12.0 GB available

118% VRAM needed

2.1 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.6 GB host RAM)

Decode

12.4 tok/s

TTFT

15636 ms

Safe context

Memory

14.1 GB / 12.0 GB

Offload

20%

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsStarCoder2 15B 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: 12.4 tok/s decode · 15.6s TTFT (warm) · 31 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	D	Very compromised	12.4 tok/s	8508 ms	4K
Coding	D	Very compromised	11.3 tok/s	17069 ms	4K
Agentic Coding	F	Too heavy	9.6 tok/s	29403 ms	4K
Reasoning	D	Very compromised	11.3 tok/s	20172 ms	4K
RAG	F	Too heavy	9.6 tok/s	36754 ms	4K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C54
Q3_K_S	3	7.4 GB	Low	C53
NVFP4Best for your GPU

Get started

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

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "bigcode/starcoder2-15b" \
 --hf-file "starcoder2-15b-Q5_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options

Hardware that runs StarCoder2 15B well

👁 Intel
Intel Arc A770 16GBBudget pick

16 GB VRAM (+4)560 GB/s (+104)

Removes host-memory offload, which is usually the single biggest latency and throughput win.26 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 110%.

~$349 MSRP

👁 Intel
Intel Arc Pro B50 16GBBest value

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.12.5 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$399 MSRP

👁 Intel
Intel Arc Pro B60 24GBIntel upgrade

24 GB VRAM (+12)

Removes host-memory offload, which is usually the single biggest latency and throughput win.25.4 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 105%.

~$599 MSRP

Frequently asked questions

See all results for Intel Arc B580 12GB See all hardware for StarCoder2 15B

URL: https://willitrunai.com/can-run/starcoder2-15b-on-arc-b580-12gb

⇱

Can StarCoder2 15B run on Intel Arc B580 12GB?

BARELY — Tight on Memory

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs StarCoder2 15B well

Frequently asked questions