Can starcoder2 15b i1 run on GTX 1080 Ti 11GB?

BARELY — Tight on Memory

D38Poor

○Estimated from fit model

starcoder2 15b i1 needs ~12.9 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.9 GB, 16.0 tok/s, Very compromised (needs ~1.4 GB host RAM)

12.9 GB required11.0 GB available

117% VRAM needed

1.9 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.4 GB host RAM)

Decode

16.0 tok/s

TTFT

12083 ms

Safe context

Memory

12.9 GB / 11.0 GB

Offload

10%

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsstarcoder2 15b i1 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: 16.0 tok/s decode · 12.1s TTFT (warm) · 40 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 {ram} 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	D	Very compromised	18.7 tok/s	5645 ms	4K
Coding	D	Very compromised	16.0 tok/s	12083 ms	4K
Agentic Coding	F	Too heavy	12.1 tok/s	23264 ms	4K
Reasoning	D	Very compromised	16.0 tok/s	14280 ms	4K
RAG	F	Too heavy	12.1 tok/s	29080 ms	4K

Quantization options

How starcoder2 15b i1 (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	C52
Q3_K_SBest for your GPU	3	7.4 GB	Low	C52

Get started

Copy-paste commands to run starcoder2 15b i1 on your machine.

Run

lms load hf-mradermacher--starcoder2-15b-i1-gguf && lms server start

Upgrade options

Hardware that runs starcoder2 15b i1 well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+5)

Removes host-memory offload, which is usually the single biggest latency and throughput win.28.7 tok/s decode

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

Raises estimated decode speed by about 79%.

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBBest value

16 GB VRAM (+5)

Removes host-memory offload, which is usually the single biggest latency and throughput win.23.5 tok/s decode

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

Raises estimated decode speed by about 47%.

~$499 MSRP

👁 NVIDIA
RTX 5070 12GBNVIDIA upgrade

12 GB VRAM (+1)672 GB/s (+188)

Raises estimated decode speed by about 77%.28.4 tok/s decode

Raises estimated decode speed by about 77%.

~$549 MSRP

Frequently asked questions

See all results for GTX 1080 Ti 11GB See all hardware for starcoder2 15b i1

URL: https://willitrunai.com/can-run/hf-mradermacher--starcoder2-15b-i1-gguf-on-gtx-1080-ti-11gb

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