Can StarCoder2 15B run on RTX 3080 10GB?

Q: What should I upgrade first if StarCoder2 15B feels slow on RTX 3080 10GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

C41Usable

Estimated from fit model

StarCoder2 15B needs ~11.3 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q3_K_S quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

StarCoder2 15B at Q4_K_M needs 13.1 GB — too much for RTX 3080 10GB (10.0 GB). Runs at Q3_K_S (11.3 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.1 GB, exceeds 10.0 GB available

13.1 GB required10.0 GB available

131% VRAM needed

3.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

26.8 tok/s

TTFT

7228 ms

Safe context

Memory

13.1 GB / 10.0 GB

Offload

20%

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder2 15B on RTX 3080 10GB
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: 26.8 tok/s decode · 7.2s TTFT (warm) · 67 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	31.0 tok/s	3407 ms	4K
Coding	F	Too heavy	26.8 tok/s	7228 ms	4K
Agentic Coding	F	Too heavy	20.6 tok/s	13703 ms	4K
Reasoning	F	Too heavy	26.8 tok/s	8543 ms	4K
RAG	F	Too heavy	20.6 tok/s	17129 ms	4K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	5.9 GB	Low	C52
Q3_K_S	3	7.4 GB	Low	F0

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 3060 12GBBest value

12 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.15.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

~$329 MSRP

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.30.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.23 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

Frequently asked questions

Can RTX 3080 10GB run StarCoder2 15B?

Yes, RTX 3080 10GB can run StarCoder2 15B at Q3_K_S quantization (Very compromised (needs ~0.9 GB host RAM)). The recommended Q4_K_M requires 13.1 GB which exceeds available memory, but at Q3_K_S it needs only 11.3 GB. Expected decode speed: 42.3 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 13.1 GB at Q4_K_M quantization. On RTX 3080 10GB, it fits at Q3_K_S using 11.3 GB.

What is the best quantization for StarCoder2 15B?

The recommended quantization is Q4_K_M, but on RTX 3080 10GB the best fitting quantization is Q3_K_S, which uses 11.3 GB.

What speed will StarCoder2 15B run at on RTX 3080 10GB?

On RTX 3080 10GB, StarCoder2 15B achieves approximately 42.3 tokens per second decode speed with a time-to-first-token of 4575ms using Q3_K_S quantization.

Can RTX 3080 10GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RTX 3080 10GB receives a F grade with 26.8 tok/s and 4K context.

What context window can StarCoder2 15B use on RTX 3080 10GB?

On RTX 3080 10GB, StarCoder2 15B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3080 10GB See all hardware for StarCoder2 15B

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