Can StarCoder2 15B run on RTX 5000 Ada Laptop 16GB?

YES — Tight Fit

C52Usable

Estimated from fit model

StarCoder2 15B needs ~13.4 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~47 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 13.4 GB, 47.0 tok/s, Tight fit

13.4 GB required16.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

47.0 tok/s

TTFT

4119 ms

Safe context

40K

Memory

13.4 GB / 16.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on RTX 5000 Ada Laptop 16GB
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: 47.0 tok/s decode · 4.1s TTFT (warm) · 118 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	47.0 tok/s	2247 ms	40K
Coding	C	Tight fit	47.0 tok/s	4119 ms	40K
Agentic Coding	C	Tight fit	47.0 tok/s	5992 ms	40K
Reasoning	C	Tight fit	47.0 tok/s	4868 ms	40K
RAG	C	Tight fit	47.0 tok/s	7490 ms	40K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C50
Q3_K_S	3	7.4 GB	Low	C51
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

Upgrade options

Hardware that runs StarCoder2 15B well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.30.7 tok/s decode

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

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+360)

Raises estimated decode speed by about 45%.68.3 tok/s decode

Raises estimated decode speed by about 45%.

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

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+432)

Raises estimated decode speed by about 82%.85.6 tok/s decode

Raises estimated decode speed by about 82%.

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

~$1,599 MSRP

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run StarCoder2 15B?

Yes, RTX 5000 Ada Laptop 16GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 47.0 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 13.4 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 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, StarCoder2 15B achieves approximately 47.0 tokens per second decode speed with a time-to-first-token of 4119ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RTX 5000 Ada Laptop 16GB receives a C grade with 47.0 tok/s and 40K context.

What context window can StarCoder2 15B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, StarCoder2 15B can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada Laptop 16GB See all hardware for StarCoder2 15B

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