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

NO — Won't Fit

F0Won't run

Estimated from fit model

StarCoder 15B needs ~27.8 GB but Intel Arc A730M 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 27.8 GB, exceeds 12.0 GB available

27.8 GB required12.0 GB available

232% VRAM needed

15.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.3 tok/s

TTFT

83005 ms

Safe context

Memory

27.8 GB / 12.0 GB

Offload

60%

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder 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: 2.3 tok/s decode · 83.0s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 27.8 GB, but this setup only exposes 12.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

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	F	Too heavy	3.8 tok/s	28024 ms	4K
Coding	F	Too heavy	2.3 tok/s	83005 ms	4K
Agentic Coding	F	Too heavy	2.3 tok/s	120734 ms	4K
Reasoning	F	Too heavy	2.3 tok/s	98096 ms	4K
RAG	F	Too heavy	2.3 tok/s	150918 ms	4K

Inference speed

StarCoder 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder 15B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~113 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q5_K_M	113.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	52.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	43.8	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	41.6	Fits
RTX 4090 24GB	24 GB	Q5_K_M	36.3	Too big
RX 7900 XTX 24GB	24 GB	Q5_K_M	32.8	Too big
RTX 3090 24GB	24 GB	Q5_K_M	31.1	Too big
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	30.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	30.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	22.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	20.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	18.3	Tight
RTX 4080 Super 16GB	16 GB	Q5_K_M	13.1	Too big
RTX 4070 12GB	12 GB	Q5_K_M	5.4	Too big
RTX 3060 12GB	12 GB	Q5_K_M	3.4	Too big
RTX 4060 8GB	8 GB	Q5_K_M	2.8	Too big

Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How StarCoder 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	A77
Q3_K_S	3	7.4 GB	Low	A77
NVFP4Best for your GPU	4	8.4 GB	Medium	A77
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

Upgrade-Optionen

Hardware, die StarCoder 15B gut ausführt

NVIDIA A100 40GBGrößter Sprung

40 GB VRAM (+28)1555 GB/s (+1219)

Makes the model fit on the accelerator instead of staying completely out of reach.123.4 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

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

ca. $10,000 MSRP

Intel Data Center GPU Max 1550 128GBGünstige Wahl

128 GB VRAM (+116)3200 GB/s (+2864)

Makes the model fit on the accelerator instead of staying completely out of reach.190.4 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

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

ca. $15,000 MSRP

Gaudi 3 128GBBestes Preis-Leistungs-Verhältnis

128 GB VRAM (+116)3700 GB/s (+3364)

Makes the model fit on the accelerator instead of staying completely out of reach.210 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

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

ca. $15,000 MSRP

Frequently asked questions

Can Intel Arc A730M 12GB run StarCoder 15B?

No, StarCoder 15B requires more memory than Intel Arc A730M 12GB provides.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 27.8 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder 15B?

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

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

On Intel Arc A730M 12GB, StarCoder 15B achieves approximately 2.3 tokens per second decode speed with a time-to-first-token of 83005ms using Q5_K_M quantization.

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

For coding workloads, StarCoder 15B on Intel Arc A730M 12GB receives a F grade with 2.3 tok/s and 4K context.

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

On Intel Arc A730M 12GB, StarCoder 15B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder 15B feels slow on Intel Arc A730M 12GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Would CUDA be a better path than Intel Arc A730M 12GB for StarCoder 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 StarCoder 15B

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