Can starcoder2 15b instruct v0.1 run on RTX 4090 24GB?

YES — Runs Great

C54Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~14.5 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~84 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, 83.7 tok/s, Runs well

14.5 GB required24.0 GB available

60% VRAM used

Fit status

Runs well

Decode

83.7 tok/s

TTFT

2312 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 4090 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: 83.7 tok/s decode · 2.3s TTFT (warm) · 209 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	83.7 tok/s	1261 ms	102K
Coding	C	Runs well	83.7 tok/s	2312 ms	102K
Agentic Coding	B	Runs well	83.7 tok/s	3363 ms	102K
Reasoning	C	Runs well	83.7 tok/s	2733 ms	102K
RAG	B	Runs well	83.7 tok/s	4204 ms	102K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX 4090 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

Frequently asked questions

Can RTX 4090 24GB run starcoder2 15b instruct v0.1?

Yes, RTX 4090 24GB can run starcoder2 15b instruct v0.1 with a C grade (Runs well). Expected decode speed: 83.7 tok/s.

How much VRAM does starcoder2 15b instruct v0.1 need?

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 14.5 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 15b instruct v0.1?

The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 15b instruct v0.1 run at on RTX 4090 24GB?

On RTX 4090 24GB, starcoder2 15b instruct v0.1 achieves approximately 83.7 tokens per second decode speed with a time-to-first-token of 2312ms using Q4_K_M quantization.

Can RTX 4090 24GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on RTX 4090 24GB receives a C grade with 83.7 tok/s and 102K context.

What context window can starcoder2 15b instruct v0.1 use on RTX 4090 24GB?

On RTX 4090 24GB, starcoder2 15b instruct v0.1 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 4090 24GB See all hardware for starcoder2 15b instruct v0.1

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