Can CodeNinja 1.0 OpenChat 7B i1 run on GTX 1080 Ti 11GB?

YES — Runs Great

C55Usable

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~7.4 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~67 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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

Q4_K_M (Medium quality) — 7.4 GB, 66.9 tok/s, Runs well

7.4 GB required11.0 GB available

67% VRAM used

Fit status

Runs well

Decode

66.9 tok/s

TTFT

2895 ms

Safe context

86K

Memory

7.4 GB / 11.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B 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: 66.9 tok/s decode · 2.9s TTFT (warm) · 167 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	66.9 tok/s	1579 ms	86K
Coding	C	Runs well	66.9 tok/s	2895 ms	86K
Agentic Coding	B	Runs well	66.9 tok/s	4211 ms	86K
Reasoning	C	Runs well	66.9 tok/s	3421 ms	86K
RAG	B	Runs well	66.9 tok/s	5263 ms	86K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (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	C49
Q3_K_S	3	3.4 GB	Low	C50
NVFP4	4

Get started

Copy-paste commands to run CodeNinja 1.0 OpenChat 7B i1 on your machine.

Run

lms load hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf && lms server start

Frequently asked questions

Can GTX 1080 Ti 11GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, GTX 1080 Ti 11GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 66.9 tok/s.

How much VRAM does CodeNinja 1.0 OpenChat 7B i1 need?

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 7.4 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeNinja 1.0 OpenChat 7B i1?

The recommended quantization for CodeNinja 1.0 OpenChat 7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeNinja 1.0 OpenChat 7B i1 run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 66.9 tokens per second decode speed with a time-to-first-token of 2895ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on GTX 1080 Ti 11GB receives a C grade with 66.9 tok/s and 86K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 86K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1080 Ti 11GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

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