Can CodeNinja 1.0 OpenChat 7B i1 run on GTX 1080 8GB?

YES — Tight Fit

C51Usable

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~7.1 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~44 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: 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) — 7.1 GB, 44.2 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

44.2 tok/s

TTFT

4379 ms

Safe context

34K

Memory

7.1 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on GTX 1080 8GB
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: 44.2 tok/s decode · 4.4s TTFT (warm) · 111 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	Tight fit	44.2 tok/s	2388 ms	34K
Coding	C	Tight fit	44.2 tok/s	4379 ms	34K
Agentic Coding	C	Runs with offload	44.2 tok/s	6369 ms	34K
Reasoning	C	Tight fit	44.2 tok/s	5175 ms	34K
RAG	C	Runs with offload	44.2 tok/s	7961 ms	34K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on GTX 1080 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	C53
Q4_K_M	4	4.3 GB	Medium	C53
Q5_K_MBest for your GPU	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

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

Opções de upgrade

Hardware que roda bem CodeNinja 1.0 OpenChat 7B i1

RTX 3060 12GBOpção econômica

12 GB VRAM (+4)360 GB/s (+40)

Raises estimated decode speed by about 26%.55.6 tok/s decodificação

Raises estimated decode speed by about 26%.

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RTX 5070 12GBMelhor custo-benefício

12 GB VRAM (+4)672 GB/s (+352)

Raises estimated decode speed by about 122%.98 tok/s decodificação

Raises estimated decode speed by about 122%.

Adds memory headroom for longer context windows and future model growth.

~$549 MSRP

RTX 4070 Super 12GBUpgrade NVIDIA

12 GB VRAM (+4)504 GB/s (+184)

Raises estimated decode speed by about 106%.90.9 tok/s decodificação

Raises estimated decode speed by about 106%.

Adds memory headroom for longer context windows and future model growth.

~$599 MSRP

Frequently asked questions

Can GTX 1080 8GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, GTX 1080 8GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Tight fit). Expected decode speed: 44.2 tok/s.

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

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 7.1 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 8GB?

On GTX 1080 8GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 44.2 tokens per second decode speed with a time-to-first-token of 4379ms using Q4_K_M quantization.

Can GTX 1080 8GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on GTX 1080 8GB receives a C grade with 44.2 tok/s and 34K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on GTX 1080 8GB?

On GTX 1080 8GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1080 8GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

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<iframe src="https://willitrunai.com/embed/hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf-on-gtx-1080-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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