Can StarCoder2 15B run on RTX 5080 Laptop 16GB?

YES — Tight Fit

C54Usable

Estimated from fit model

StarCoder2 15B needs ~14.8 GB VRAM. RTX 5080 Laptop 16GB has 16.0 GB. With Q5_K_M quantization, expect ~67 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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) — 14.8 GB, 66.5 tok/s, Tight fit

14.8 GB required16.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

66.5 tok/s

TTFT

2911 ms

Safe context

16K

Memory

14.8 GB / 16.0 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on RTX 5080 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: 66.5 tok/s decode · 2.9s TTFT (warm) · 166 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	66.5 tok/s	1588 ms	16K
Coding	C	Tight fit	66.5 tok/s	2911 ms	16K
Agentic Coding	C	Runs with offload (needs ~0 GB host RAM)	49.6 tok/s	5676 ms	16K
Reasoning	C	Tight fit	66.5 tok/s	3440 ms	16K
RAG	C	Runs with offload (needs ~0 GB host RAM)	49.6 tok/s	7095 ms	16K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on RTX 5080 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C51
Q3_K_S	3	7.4 GB	Low	C53
NVFP4	4	8.4 GB	Medium	C53
Q4_K_M	4	9.2 GB	Medium	C53
Q5_K_M	5	10.8 GB	High	C52
Q6_KBest for your GPU	6	12.3 GB	High	C52
Q8_0	8	16.1 GB	Very High	F0
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

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.29 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 (+168)

Adds memory headroom for longer context windows and future model growth.67.6 tok/s decode

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 (+240)

Adds memory headroom for longer context windows and future model growth.79 tok/s decode

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

~$1,599 MSRP

Frequently asked questions

Can RTX 5080 Laptop 16GB run StarCoder2 15B?

Yes, RTX 5080 Laptop 16GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 66.5 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.8 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 RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, StarCoder2 15B achieves approximately 66.5 tokens per second decode speed with a time-to-first-token of 2911ms using Q5_K_M quantization.

Can RTX 5080 Laptop 16GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RTX 5080 Laptop 16GB receives a C grade with 66.5 tok/s and 16K context.

What context window can StarCoder2 15B use on RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, 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.

What should I upgrade first if StarCoder2 15B feels slow on RTX 5080 Laptop 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 5080 Laptop 16GB See all hardware for StarCoder2 15B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/starcoder2-15b-on-rtx-5080-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

66.5 tok/s

1588 ms

16K

Coding

Tight fit

66.5 tok/s

2911 ms

16K

Agentic Coding

Runs with offload (needs ~0 GB host RAM)

49.6 tok/s

5676 ms

16K

Reasoning

Tight fit

66.5 tok/s

3440 ms

16K

RAG

Runs with offload (needs ~0 GB host RAM)

49.6 tok/s

7095 ms

16K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on RTX 5080 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C51
Q3_K_S	3	7.4 GB	Low	C53
NVFP4	4	8.4 GB	Medium	C53
Q4_K_M	4	9.2 GB	Medium	C53
Q5_K_M	5	10.8 GB	High	C52
Q6_KBest for your GPU	6	12.3 GB	High	C52
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

Frequently asked questions

Can RTX 5080 Laptop 16GB run StarCoder2 15B?

Yes, RTX 5080 Laptop 16GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 66.5 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.8 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 RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, StarCoder2 15B achieves approximately 66.5 tokens per second decode speed with a time-to-first-token of 2911ms using Q5_K_M quantization.

Can RTX 5080 Laptop 16GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RTX 5080 Laptop 16GB receives a C grade with 66.5 tok/s and 16K context.

What context window can StarCoder2 15B use on RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, 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.

What should I upgrade first if StarCoder2 15B feels slow on RTX 5080 Laptop 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.