Can starcoder2 15b instruct v0.1 run on RTX 4500 Ada 24GB?

YES — Runs Great

C52Usable

○Estimated from fit model

starcoder2 15b instruct v0.1 needs ~14.5 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~37 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) — 14.5 GB, 37.3 tok/s, Runs well

14.5 GB required24.0 GB available

60% VRAM used

Fit status

Runs well

Decode

37.3 tok/s

TTFT

5191 ms

Safe context

102K

Memory

14.5 GB / 24.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 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: 37.3 tok/s decode · 5.2s TTFT (warm) · 93 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	37.3 tok/s	2831 ms	102K
Coding	C	Runs well	37.3 tok/s	5191 ms	102K
Agentic Coding	C	Runs well	37.3 tok/s	7550 ms	102K
Reasoning	C	Runs well	37.3 tok/s	6134 ms	102K
RAG	C	Runs well	37.3 tok/s	9437 ms	102K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C46
Q3_K_S	3	7.4 GB	Low	C47
NVFP4	4

Get started

Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.

Run

lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server start

Upgrade options

Hardware that runs starcoder2 15b instruct v0.1 well

👁 NVIDIA
RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1360)

Raises estimated decode speed by about 252%.131.2 tok/s decode

Raises estimated decode speed by about 252%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

AMD Instinct MI100 32GBBest value

32 GB VRAM (+8)1228 GB/s (+796)

Raises estimated decode speed by about 134%.87.2 tok/s decode

Raises estimated decode speed by about 134%.