Can StarCoder2 15B run on RTX 5090 Laptop 24GB?

YES — Runs Great

C54Usable

Estimated from fit model

StarCoder2 15B needs ~14.5 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~82 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

Q4_K_M (Medium quality) — 14.5 GB, 82.3 tok/s, Runs well

14.5 GB required24.0 GB available

60% VRAM used

Fit status

Runs well

Decode

82.3 tok/s

TTFT

2354 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 on RTX 5090 Laptop 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: 82.3 tok/s decode · 2.4s TTFT (warm) · 206 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	82.3 tok/s	1284 ms	102K
Coding	C	Runs well	82.3 tok/s	2354 ms	102K
Agentic Coding	B	Runs well	82.3 tok/s	3423 ms	102K
Reasoning	C	Runs well	82.3 tok/s	2782 ms	102K
RAG	B	Runs well	82.3 tok/s	4279 ms	102K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on RTX 5090 Laptop 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 on your machine.

Run

lms load hf-second-state--starcoder2-15b-gguf && lms server start

Frequently asked questions

Can RTX 5090 Laptop 24GB run StarCoder2 15B?

Yes, RTX 5090 Laptop 24GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 82.3 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.5 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, StarCoder2 15B achieves approximately 82.3 tokens per second decode speed with a time-to-first-token of 2354ms using Q4_K_M quantization.

Can RTX 5090 Laptop 24GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RTX 5090 Laptop 24GB receives a C grade with 82.3 tok/s and 102K context.

What context window can StarCoder2 15B use on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, StarCoder2 15B can safely use up to 102K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5090 Laptop 24GB See all hardware for StarCoder2 15B

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