Can StarCoder2 15B run on Intel Arc A730M 12GB?

BARELY — Tight on Memory

D32Poor

Estimated from fit model

StarCoder2 15B needs ~14.1 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q5_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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, 9.0 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

9.0 tok/s

TTFT

21420 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 A730M 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: 9.0 tok/s decode · 21.4s TTFT (warm) · 23 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 (needs ~1.2 GB host RAM)	9.9 tok/s	10646 ms	4K
Coding	D	Very compromised (needs ~1.6 GB host RAM)	9.0 tok/s	21420 ms	4K
Agentic Coding	F	Too heavy	7.6 tok/s	37097 ms	4K
Reasoning	D	Very compromised (needs ~1.6 GB host RAM)	9.0 tok/s	25314 ms	4K
RAG	F	Too heavy	7.6 tok/s	46371 ms	4K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on Intel Arc A730M 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	4	8.4 GB	Medium	C53
Q4_K_M	4	9.2 GB	Medium	F0
Q5_K_M	5	10.8 GB	High	F0
Q6_K	6	12.3 GB	High	F0
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

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

Opciones de mejora

Hardware que ejecuta bien StarCoder2 15B

Intel Arc A770 16GBOpción económica

16 GB VRAM (+4)560 GB/s (+224)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.26 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 189%.

~$349 MSRP

Intel Arc Pro B50 16GBMejor relación calidad-precio

16 GB VRAM (+4)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.12.5 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 39%.

~$399 MSRP

Intel Arc Pro B60 24GBMejora Intel

24 GB VRAM (+12)456 GB/s (+120)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.25.4 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 182%.

~$599 MSRP

Frequently asked questions

Can Intel Arc A730M 12GB run StarCoder2 15B?

Yes, Intel Arc A730M 12GB can run StarCoder2 15B with a D grade (Very compromised (needs ~1.6 GB host RAM)). Expected decode speed: 9.0 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.1 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on Intel Arc A730M 12GB?

On Intel Arc A730M 12GB, StarCoder2 15B achieves approximately 9.0 tokens per second decode speed with a time-to-first-token of 21420ms using Q5_K_M quantization.

Can Intel Arc A730M 12GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on Intel Arc A730M 12GB receives a D grade with 9.0 tok/s and 4K context.

What context window can StarCoder2 15B use on Intel Arc A730M 12GB?

On Intel Arc A730M 12GB, StarCoder2 15B can safely use up to 4K 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 15B feels slow on Intel Arc A730M 12GB?

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.

Would CUDA be a better path than Intel Arc A730M 12GB 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 A730M 12GB See all hardware for StarCoder2 15B

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