Can StarCoder2 15B run on RTX A5000 24GB?

YES — Runs Great

B55Good

○Estimated from fit model

StarCoder2 15B needs ~15.6 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q5_K_M quantization, expect ~51 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 15.6 GB, 55.4 tok/s, Runs well

15.6 GB required24.0 GB available

65% VRAM used

Fit status

Runs well

Decode

55.4 tok/s

TTFT

3493 ms

Safe context

16K

Memory

15.6 GB / 24.0 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on RTX A5000 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: 55.4 tok/s decode · 3.5s TTFT (warm) · 139 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	C	Runs well	50.8 tok/s	2080 ms	16K
Coding	B	Runs well	50.8 tok/s	3813 ms	16K
Agentic Coding	B	Runs well	50.8 tok/s	5546 ms	16K
Reasoning	B	Runs well	50.8 tok/s	4506 ms	16K
RAG	B	Runs well	50.8 tok/s	6933 ms	16K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C47
Q3_K_S	3	7.4 GB	Low	C48
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

Frequently asked questions

See all results for RTX A5000 24GB See all hardware for StarCoder2 15B

URL: https://willitrunai.com/can-run/starcoder2-15b-on-a5000-24gb

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