Can StarCoder 15B run on Intel Arc Pro B60 24GB?

YES — With Q4_K_M

B63Good

Estimated from fit model

StarCoder 15B needs ~27.4 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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.

StarCoder 15B at Q5_K_M needs 29.0 GB — too much for Intel Arc Pro B60 24GB (24.0 GB). Runs at Q4_K_M (27.4 GB) with medium quality. 4 quantization levels fit.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 29.0 GB, exceeds 24.0 GB available

29.0 GB required24.0 GB available

121% VRAM needed

5.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.0 tok/s

TTFT

16070 ms

Safe context

Memory

29.0 GB / 24.0 GB

Offload

20%

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder 15B 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: 12.0 tok/s decode · 16.1s TTFT (warm) · 30 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 10% 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	A	Tight fit	23.3 tok/s	4541 ms	8K
Coding	F	Too heavy	12.0 tok/s	16070 ms	8K
Agentic Coding	F	Too heavy	5.2 tok/s	53764 ms	8K
Reasoning	F	Too heavy	12.0 tok/s	18992 ms	8K
RAG	F	Too heavy	5.2 tok/s	67205 ms	8K

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 Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	A71
Q3_K_S	3	7.4 GB	Low	A72
NVFP4	4	8.4 GB	Medium	A73
Q4_K_M	4	9.2 GB	Medium	A73
Q5_K_M	5	10.8 GB	High	A74
Q6_K	6	12.3 GB	High	A75
Q8_0Best for your GPU	8	16.1 GB	Very High	A75
F16	16	30.7 GB	Maximum	F0

Get started

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

Run

lms load starcoder && lms server start

升级选项

能流畅运行 StarCoder 15B 的硬件

NVIDIA A100 40GB最大飞跃

40 GB VRAM (+16)1555 GB/s (+1099)

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

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.

~$10,000 MSRP

Intel Data Center GPU Max 1550 128GB经济之选

128 GB VRAM (+104)3200 GB/s (+2744)

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

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.

~$15,000 MSRP

Gaudi 3 128GB最佳性价比

128 GB VRAM (+104)3700 GB/s (+3244)

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

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.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run StarCoder 15B?

Yes, Intel Arc Pro B60 24GB can run StarCoder 15B at Q4_K_M quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q5_K_M requires 29.0 GB which exceeds available memory, but at Q4_K_M it needs only 27.4 GB. Expected decode speed: 15.7 tok/s.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 29.0 GB at Q5_K_M quantization. On Intel Arc Pro B60 24GB, it fits at Q4_K_M using 27.4 GB.

What is the best quantization for StarCoder 15B?

The recommended quantization is Q5_K_M, but on Intel Arc Pro B60 24GB the best fitting quantization is Q4_K_M, which uses 27.4 GB.

What speed will StarCoder 15B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, StarCoder 15B achieves approximately 15.7 tokens per second decode speed with a time-to-first-token of 12325ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on Intel Arc Pro B60 24GB receives a F grade with 12.0 tok/s and 8K context.

What context window can StarCoder 15B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, StarCoder 15B can safely use up to 8K tokens of context at Q4_K_M quantization. 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 Pro B60 24GB?

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 Pro B60 24GB 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 Pro B60 24GB See all hardware for StarCoder 15B

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