Can StarCoder2 15B run on NVIDIA L4 24GB?

YES — Runs Great

C52Usable

Estimated from fit model

StarCoder2 15B needs ~15.6 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q5_K_M quantization, expect ~20 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

Q5_K_M (High quality) — 15.6 GB, 20.1 tok/s, Runs well

15.6 GB required24.0 GB available

65% VRAM used

Fit status

Runs well

Decode

20.1 tok/s

TTFT

9630 ms

Safe context

16K

Memory

15.6 GB / 24.0 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsStarCoder2 15B 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: 20.1 tok/s decode · 9.6s TTFT (warm) · 50 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	20.1 tok/s	5252 ms	16K
Coding	C	Runs well	20.1 tok/s	9630 ms	16K
Agentic Coding	C	Runs well	20.1 tok/s	14007 ms	16K
Reasoning	C	Runs well	20.1 tok/s	11380 ms	16K
RAG	C	Runs well	20.1 tok/s	17508 ms	16K

Quantization options

How StarCoder2 15B (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	C47
Q3_K_S	3	7.4 GB	Low	C48
NVFP4	4	8.4 GB	Medium	C49
Q4_K_M	4	9.2 GB	Medium	C49
Q5_K_M	5	10.8 GB	High	C51
Q6_K	6	12.3 GB	High	C52
Q8_0Best for your GPU	8	16.1 GB	Very High	C51
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bigcode/starcoder2-15b" \
  --hf-file "starcoder2-15b-Q5_K_M.gguf" \
  -c 4096 -ngl 99

アップグレードオプション

StarCoder2 15Bを快適に動かすハードウェア

RTX 5090 32GBコスパ重視

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

Raises estimated decode speed by about 516%.123.8 tok/sデコード

Raises estimated decode speed by about 516%.

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

〜$1,999 MSRP

RTX PRO 4500 Blackwell 32GBコスパ最良

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

Raises estimated decode speed by about 286%.77.6 tok/sデコード

Raises estimated decode speed by about 286%.

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

〜$2,499 MSRP

NVIDIA V100 32GBNVIDIAアップグレード

32 GB VRAM (+8)900 GB/s (+600)

Raises estimated decode speed by about 209%.62.2 tok/sデコード

Raises estimated decode speed by about 209%.

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

〜$8,999 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run StarCoder2 15B?

Yes, NVIDIA L4 24GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 20.1 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 15.6 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, StarCoder2 15B achieves approximately 20.1 tokens per second decode speed with a time-to-first-token of 9630ms using Q5_K_M quantization.

Can NVIDIA L4 24GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on NVIDIA L4 24GB receives a C grade with 20.1 tok/s and 16K context.

What context window can StarCoder2 15B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, StarCoder2 15B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for StarCoder2 15B

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