Can StarCoder2 7B run on RTX 4500 Ada 24GB?

YES — Runs Great

C48Usable

○Estimated from fit model

StarCoder2 7B needs ~8.4 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~87 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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

Q4_K_M (Medium quality) — 8.4 GB, 87.3 tok/s, Runs well

8.4 GB required24.0 GB available

35% VRAM used

Fit status

Runs well

Decode

87.3 tok/s

TTFT

2219 ms

Safe context

16K

Memory

8.4 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on RTX 4500 Ada 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: 87.3 tok/s decode · 2.2s TTFT (warm) · 218 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	87.3 tok/s	1210 ms	16K
Coding	C	Runs well	87.3 tok/s	2219 ms	16K
Agentic Coding	C	Runs well	87.3 tok/s	3227 ms	16K
Reasoning	C	Runs well	87.3 tok/s	2622 ms	16K
RAG	C	Runs well	87.3 tok/s	4034 ms	16K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C44
Q3_K_S	3	3.4 GB	Low	C44
NVFP4	4	3.9 GB	Medium	C44
Q4_K_M	4	4.3 GB	Medium	C44
Q5_K_M	5	5.0 GB	High	C45
Q6_K	6	5.7 GB	High	C45
Q8_0	8	7.5 GB	Very High	C46
F16Best for your GPU	16	14.3 GB	Maximum	C49

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load starcoder2-7b && lms server start

Frequently asked questions

See all results for RTX 4500 Ada 24GB See all hardware for StarCoder2 7B

URL: https://willitrunai.com/can-run/starcoder2-7b-on-rtx-4500-ada-24gb