Can CodeLlama 7B Instruct run on GTX 1080 Ti 11GB?

Q: What should I upgrade first if CodeLlama 7B Instruct 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 Q2_K

B65Good

Estimated from fit model

CodeLlama 7B Instruct needs ~12.8 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q2_K quantization, expect ~46 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.

CodeLlama 7B Instruct at Q4_K_M needs 14.4 GB — too much for GTX 1080 Ti 11GB (11.0 GB). Runs at Q2_K (12.8 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 14.4 GB, exceeds 11.0 GB available

14.4 GB required11.0 GB available

131% VRAM needed

3.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

27.1 tok/s

TTFT

7151 ms

Safe context

Memory

14.4 GB / 11.0 GB

Offload

20%

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeLlama 7B Instruct 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: 27.1 tok/s decode · 7.2s TTFT (warm) · 68 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 0.4 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	Runs with offload	66.9 tok/s	1579 ms	9K
Coding	F	Too heavy	27.1 tok/s	7151 ms	9K
Agentic Coding	F	Too heavy	10.4 tok/s	26969 ms	9K
Reasoning	F	Too heavy	27.1 tok/s	8451 ms	9K
RAG	F	Too heavy	10.4 tok/s	33712 ms	9K

Quantization options

How CodeLlama 7B Instruct (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 CodeLlama 7B Instruct on your machine.

Run

lms load CodeLlama-7b-Instruct-hf && lms server start

Upgrade options

Hardware that runs CodeLlama 7B Instruct well

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 16GBBest value

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

RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.51.3 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.

~$625 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run CodeLlama 7B Instruct?

Yes, GTX 1080 Ti 11GB can run CodeLlama 7B Instruct at Q2_K quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 14.4 GB which exceeds available memory, but at Q2_K it needs only 12.8 GB. Expected decode speed: 46.2 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 14.4 GB at Q4_K_M quantization. On GTX 1080 Ti 11GB, it fits at Q2_K using 12.8 GB.

What is the best quantization for CodeLlama 7B Instruct?

The recommended quantization is Q4_K_M, but on GTX 1080 Ti 11GB the best fitting quantization is Q2_K, which uses 12.8 GB.

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

On GTX 1080 Ti 11GB, CodeLlama 7B Instruct achieves approximately 46.2 tokens per second decode speed with a time-to-first-token of 4194ms using Q2_K quantization.

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

For coding workloads, CodeLlama 7B Instruct on GTX 1080 Ti 11GB receives a F grade with 27.1 tok/s and 9K context.

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

On GTX 1080 Ti 11GB, CodeLlama 7B Instruct can safely use up to 12K tokens of context at Q2_K quantization. The model's official context limit is 16K, but available memory constrains the safe maximum.

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

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