Can StarCoder2 15B run on GTX 1080 Ti 11GB?

YES — With Q4_K_M

C40Usable

Estimated from fit model

StarCoder2 15B needs ~12.7 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~18 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 Q5_K_M needs 14.3 GB — too much for GTX 1080 Ti 11GB (11.0 GB). Runs at Q4_K_M (12.7 GB) with medium quality. 4 quantization levels fit.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 14.3 GB, exceeds 11.0 GB available

14.3 GB required11.0 GB available

130% VRAM needed

3.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.0 tok/s

TTFT

16090 ms

Safe context

Memory

14.3 GB / 11.0 GB

Offload

20%

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder2 15B on GTX 1080 Ti 11GB
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: 12.0 tok/s decode · 16.1s TTFT (warm) · 30 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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 1.2 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	13.2 tok/s	7976 ms	4K
Coding	F	Too heavy	12.0 tok/s	16090 ms	4K
Agentic Coding	F	Too heavy	10.1 tok/s	28010 ms	4K
Reasoning	F	Too heavy	12.0 tok/s	19016 ms	4K
RAG	F	Too heavy	10.1 tok/s	35012 ms	4K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C54
Q3_K_SBest for your GPU	3	7.4 GB	Low	C53
NVFP4	4	8.4 GB	Medium	F0
Q4_K_M	4	9.2 GB	Medium	F0
Q5_K_M	5	10.8 GB	High	F0
Q6_K	6	12.3 GB	High	F0
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

Opções de upgrade

Hardware que roda bem StarCoder2 15B

RTX 5060 Ti 16GBOpção econômica

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.28.6 tok/s decodificação

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 16GBMelhor custo-benefício

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.21.7 tok/s decodificação

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

RTX 2000 Ada 16GBUpgrade NVIDIA

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.22.6 tok/s decodificação

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.

~$625 MSRP

RTX 4090 24GBMaior salto

24 GB VRAM (+13)1008 GB/s (+524)

Makes the model fit on the accelerator instead of staying completely out of reach.79 tok/s decodificação

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.

~$1,599 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run StarCoder2 15B?

Yes, GTX 1080 Ti 11GB can run StarCoder2 15B at Q4_K_M quantization (Very compromised (needs ~1.2 GB host RAM)). The recommended Q5_K_M requires 14.3 GB which exceeds available memory, but at Q4_K_M it needs only 12.7 GB. Expected decode speed: 18.2 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.3 GB at Q5_K_M quantization. On GTX 1080 Ti 11GB, it fits at Q4_K_M using 12.7 GB.

What is the best quantization for StarCoder2 15B?

The recommended quantization is Q5_K_M, but on GTX 1080 Ti 11GB the best fitting quantization is Q4_K_M, which uses 12.7 GB.

What speed will StarCoder2 15B run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, StarCoder2 15B achieves approximately 18.2 tokens per second decode speed with a time-to-first-token of 10627ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on GTX 1080 Ti 11GB receives a F grade with 12.0 tok/s and 4K context.

What context window can StarCoder2 15B use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, StarCoder2 15B can safely use up to 4K tokens of context at Q4_K_M quantization. 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 GTX 1080 Ti 11GB?

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.

See all results for GTX 1080 Ti 11GB See all hardware for StarCoder2 15B

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