Can StarCoder 15B run on NVIDIA H20 96GB?

YES — Runs Great

A75Great

○Estimated from fit model

StarCoder 15B needs ~36.2 GB VRAM. NVIDIA H20 96GB has 96.0 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) — 36.2 GB, 210.0 tok/s, Runs well

36.2 GB required96.0 GB available

38% VRAM used

Fit status

Runs well

Decode

210.0 tok/s

TTFT

922 ms

Safe context

Memory

36.2 GB / 96.0 GB

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsStarCoder 15B on NVIDIA H20 96GB
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 NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	B64
Q3_K_S	3	7.4 GB	Low	B64
NVFP4	4	8.4 GB	Medium	B64
Q4_K_M	4	9.2 GB	Medium	B64
Q5_K_M	5	10.8 GB	High	B65
Q6_K	6	12.3 GB	High	B65
Q8_0	8	16.1 GB	Very High	B65
F16Best for your GPU	16	30.7 GB	Maximum	B67

Get started

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

Run

lms load starcoder && lms server start

Your hardware

More models your NVIDIA H20 96GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	47 tok/s
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	489.9 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	212.5 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	213.1 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	130.3 tok/s

Frequently asked questions

See all results for NVIDIA H20 96GB See all hardware for StarCoder 15B

URL: https://willitrunai.com/can-run/starcoder-15b-on-h20-96gb