Can StarCoder 7B run on GTX 1080 Ti 11GB?

Q: What should I upgrade first if StarCoder 7B 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.

YES — With Q3_K_S

B58Good

Estimated from fit model

StarCoder 7B needs ~13.1 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q3_K_S quantization, expect ~39 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.

StarCoder 7B at Q4_K_M needs 13.9 GB — too much for GTX 1080 Ti 11GB (11.0 GB). Runs at Q3_K_S (13.1 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.9 GB, exceeds 11.0 GB available

13.9 GB required11.0 GB available

126% VRAM needed

2.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

29.2 tok/s

TTFT

6629 ms

Safe context

Memory

13.9 GB / 11.0 GB

Offload

20%

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder 7B 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: 29.2 tok/s decode · 6.6s TTFT (warm) · 73 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 20% 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 0.5 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	A	Tight fit	66.9 tok/s	1579 ms	8K
Coding	F	Too heavy	29.2 tok/s	6629 ms	8K
Agentic Coding	F	Too heavy	11.5 tok/s	24432 ms	8K
Reasoning	F	Too heavy	29.2 tok/s	7834 ms	8K
RAG	F	Too heavy	11.5 tok/s	30540 ms	8K

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A73
Q3_K_S	3	3.4 GB	Low	A74
NVFP4	4

Get started

Copy-paste commands to run StarCoder 7B on your machine.

Run

lms load starcoder-7b && lms server start

Upgrade options

Hardware that runs StarCoder 7B well

RTX 3060 12GBBest value

12 GB VRAM (+1)

Makes the model fit on the accelerator instead of staying completely out of reach.30.2 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 (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.65 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 (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.49.2 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 GTX 1080 Ti 11GB run StarCoder 7B?

Yes, GTX 1080 Ti 11GB can run StarCoder 7B at Q3_K_S quantization (Very compromised (needs ~0.5 GB host RAM)). The recommended Q4_K_M requires 13.9 GB which exceeds available memory, but at Q3_K_S it needs only 13.1 GB. Expected decode speed: 38.8 tok/s.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 13.9 GB at Q4_K_M quantization. On GTX 1080 Ti 11GB, it fits at Q3_K_S using 13.1 GB.

What is the best quantization for StarCoder 7B?

The recommended quantization is Q4_K_M, but on GTX 1080 Ti 11GB the best fitting quantization is Q3_K_S, which uses 13.1 GB.

What speed will StarCoder 7B run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, StarCoder 7B achieves approximately 38.8 tokens per second decode speed with a time-to-first-token of 4993ms using Q3_K_S quantization.

Can GTX 1080 Ti 11GB run StarCoder 7B for coding?

For coding workloads, StarCoder 7B on GTX 1080 Ti 11GB receives a F grade with 29.2 tok/s and 8K context.

What context window can StarCoder 7B use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, StarCoder 7B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for GTX 1080 Ti 11GB See all hardware for StarCoder 7B

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