Can starcoder2 15b instruct v0.1 run on NVIDIA L4 24GB?

YES — Runs Great

C50Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~14.5 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~21 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, 21.3 tok/s, Runs well

14.5 GB required24.0 GB available

60% VRAM used

Fit status

Runs well

Decode

21.3 tok/s

TTFT

9084 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 NVIDIA L4 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: 21.3 tok/s decode · 9.1s TTFT (warm) · 53 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	21.3 tok/s	4955 ms	102K
Coding	C	Runs well	21.3 tok/s	9084 ms	102K
Agentic Coding	C	Runs well	21.3 tok/s	13214 ms	102K
Reasoning	C	Runs well	21.3 tok/s	10736 ms	102K
RAG	C	Runs well	21.3 tok/s	16517 ms	102K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on NVIDIA L4 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	8.4 GB	Medium	C47
Q4_K_M	4	9.2 GB	Medium	C48
Q5_K_M	5	10.8 GB	High	C49
Q6_K	6	12.3 GB	High	C50
Q8_0Best for your GPU	8	16.1 GB	Very High	C49
F16	16	30.7 GB	Maximum	F0

Get started

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

Run

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

Upgrade-Optionen

Hardware, die starcoder2 15b instruct v0.1 gut ausführt

RTX 5090 32GBGünstige Wahl

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 516%.131.2 tok/s Dekodierung

Raises estimated decode speed by about 516%.

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

ca. $1,999 MSRP

RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 286%.82.3 tok/s Dekodierung

Raises estimated decode speed by about 286%.

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

ca. $2,499 MSRP

RTX 5000 Ada 32GBNVIDIA-Upgrade

32 GB VRAM (+8)576 GB/s (+276)

Raises estimated decode speed by about 137%.50.4 tok/s Dekodierung

Raises estimated decode speed by about 137%.

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

ca. $4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run starcoder2 15b instruct v0.1?

Yes, NVIDIA L4 24GB can run starcoder2 15b instruct v0.1 with a C grade (Runs well). Expected decode speed: 21.3 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, starcoder2 15b instruct v0.1 achieves approximately 21.3 tokens per second decode speed with a time-to-first-token of 9084ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on NVIDIA L4 24GB receives a C grade with 21.3 tok/s and 102K context.

What context window can starcoder2 15b instruct v0.1 use on NVIDIA L4 24GB?

On NVIDIA L4 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 NVIDIA L4 24GB See all hardware for starcoder2 15b instruct v0.1

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