Would CUDA be a better path than Intel Arc Pro B50 16GB 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.

Can StarCoder2 15B run on Intel Arc Pro B50 16GB?

Q: What should I upgrade first if StarCoder2 15B feels slow on Intel Arc Pro B50 16GB?

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 — Tight Fit

C49Usable

Estimated from fit model

StarCoder2 15B needs ~14.5 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q5_K_M quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 14.5 GB, 12.5 tok/s, Tight fit

14.5 GB required16.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

12.5 tok/s

TTFT

15524 ms

Safe context

16K

Memory

14.5 GB / 16.0 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on Intel Arc Pro B50 16GB
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: 12.5 tok/s decode · 15.5s TTFT (warm) · 31 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	Tight fit	11.4 tok/s	9244 ms	16K
Coding	C	Tight fit	11.4 tok/s	16947 ms	16K
Agentic Coding	C	Runs with offload	11.4 tok/s	24650 ms	16K
Reasoning	C	Tight fit	11.4 tok/s	20028 ms	16K
RAG	C	Runs with offload	11.4 tok/s	30812 ms	16K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C51
Q3_K_S	3	7.4 GB	Low	C53
NVFP4	4

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 Arc Pro B60 24GBBudget pick

24 GB VRAM (+8)456 GB/s (+232)

Raises estimated decode speed by about 103%.25.4 tok/s decode

Raises estimated decode speed by about 103%.

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

~$599 MSRP

MacBook Pro M4 32GBBest value

32 GB Unified (+16)

Adds memory headroom for longer context windows and future model growth.8.2 tok/s decode

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

~$799 MSRP

Frequently asked questions

Can Intel Arc Pro B50 16GB run StarCoder2 15B?

Yes, Intel Arc Pro B50 16GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 11.4 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.5 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 Pro B50 16GB?

On Intel Arc Pro B50 16GB, StarCoder2 15B achieves approximately 11.4 tokens per second decode speed with a time-to-first-token of 16947ms using Q5_K_M quantization.

Can Intel Arc Pro B50 16GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on Intel Arc Pro B50 16GB receives a C grade with 11.4 tok/s and 16K context.

What context window can StarCoder2 15B use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, StarCoder2 15B 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 15B feels slow on Intel Arc Pro B50 16GB?

See all results for Intel Arc Pro B50 16GB See all hardware for StarCoder2 15B

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