Can StarCoder 15B run on H100 NVL 188GB?

YES — Runs Great

A72Great

Estimated from fit model

StarCoder 15B needs ~45.4 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q5_K_M quantization, expect ~210 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

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) — 45.4 GB, 210.0 tok/s, Runs well

45.4 GB required188.0 GB available

24% VRAM used

Fit status

Runs well

Decode

210.0 tok/s

TTFT

922 ms

Safe context

Memory

45.4 GB / 188.0 GB

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsStarCoder 15B on H100 NVL 188GB
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

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	210.0 tok/s	503 ms	8K
Coding	A	Runs well	210.0 tok/s	922 ms	8K
Agentic Coding	A	Runs well	210.0 tok/s	1341 ms	8K
Reasoning	A	Runs well	210.0 tok/s	1090 ms	8K
RAG	A	Runs well	210.0 tok/s	1676 ms	8K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	B62
Q3_K_S	3	7.4 GB	Low	B62
NVFP4	4

Get started

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

Run

lms load starcoder && lms server start

Your hardware

More models your H100 NVL 188GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	91.6 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can H100 NVL 188GB run StarCoder 15B?

Yes, H100 NVL 188GB can run StarCoder 15B with a A grade (Runs well). Expected decode speed: 210.0 tok/s.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 45.4 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 H100 NVL 188GB?

On H100 NVL 188GB, StarCoder 15B achieves approximately 210.0 tokens per second decode speed with a time-to-first-token of 922ms using Q5_K_M quantization.

Can H100 NVL 188GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on H100 NVL 188GB receives a A grade with 210.0 tok/s and 8K context.

What context window can StarCoder 15B use on H100 NVL 188GB?

On H100 NVL 188GB, StarCoder 15B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for H100 NVL 188GB See all hardware for StarCoder 15B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/starcoder-15b-on-h100-nvl-188gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: