Will It Run AI

Can starcoder2 15b i1 run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

C46Usable
Estimated from fit model

starcoder2 15b i1 needs ~24.6 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~210 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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

Q4_K_M (Medium quality) — 24.6 GB, 210.0 tok/s, Runs well
24.6 GB required128.0 GB available
19% VRAM used

Fit status

Runs well

Decode

210.0 tok/s

TTFT

922 ms

Safe context

957K

Memory

24.6 GB / 128.0 GB

Memory breakdown

Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsstarcoder2 15b i1 on Intel Data Center GPU Max 1550 128GB
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: 210.0 tok/s decode · 922ms TTFT (warm) · 525 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well210.0 tok/s503 ms957K
CodingCRuns well210.0 tok/s922 ms957K
Agentic CodingCRuns well210.0 tok/s1341 ms957K
ReasoningCRuns well210.0 tok/s1090 ms957K
RAGCRuns well210.0 tok/s1676 ms957K

Quantization options

How starcoder2 15b i1 (15B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.9 GB
LowD38
Q3_K_S
3
7.4 GB
LowD38
NVFP4
4
8.4 GB
MediumD38
Q4_K_M
4
9.2 GB
MediumD38
Q5_K_M
5
10.8 GB
HighD38
Q6_K
6
12.3 GB
HighD38
Q8_0
8
16.1 GB
Very HighD38
F16Best for your GPU
16
30.7 GB
MaximumC40

Get started

Copy-paste commands to run starcoder2 15b i1 on your machine.

Run

lms load hf-mradermacher--starcoder2-15b-i1-gguf && lms server start

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run starcoder2 15b i1?

Yes, Intel Data Center GPU Max 1550 128GB can run starcoder2 15b i1 with a C grade (Runs well). Expected decode speed: 210.0 tok/s.

How much VRAM does starcoder2 15b i1 need?

starcoder2 15b i1 (15B parameters) requires approximately 24.6 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 15b i1?

The recommended quantization for starcoder2 15b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 15b i1 run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, starcoder2 15b i1 achieves approximately 210.0 tokens per second decode speed with a time-to-first-token of 922ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run starcoder2 15b i1 for coding?

For coding workloads, starcoder2 15b i1 on Intel Data Center GPU Max 1550 128GB receives a C grade with 210.0 tok/s and 957K context.

What context window can starcoder2 15b i1 use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, starcoder2 15b i1 can safely use up to 957K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if starcoder2 15b i1 feels slow on Intel Data Center GPU Max 1550 128GB?

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.

Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB for starcoder2 15b i1?

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 Data Center GPU Max 1550 128GBSee all hardware for starcoder2 15b i1
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