Can Codestral RAG 19B Pruned i1 run on GTX 1080 Ti 11GB?

Q: What should I upgrade first if Codestral RAG 19B Pruned i1 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

D38Poor

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~11.9 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q2_K quantization, expect ~20 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.

Codestral RAG 19B Pruned i1 at Q4_K_M needs 16.1 GB — too much for GTX 1080 Ti 11GB (11.0 GB). Runs at Q2_K (11.9 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 16.1 GB, exceeds 11.0 GB available

16.1 GB required11.0 GB available

146% VRAM needed

5.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.8 tok/s

TTFT

24922 ms

Safe context

Memory

16.1 GB / 11.0 GB

Offload

30%

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral RAG 19B Pruned 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: 7.8 tok/s decode · 24.9s TTFT (warm) · 19 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 0.6 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	F	Too heavy	9.1 tok/s	11617 ms	4K
Coding	F	Too heavy	7.8 tok/s	24922 ms	4K
Agentic Coding	F	Too heavy	5.8 tok/s	48165 ms	4K
Reasoning	F	Too heavy	7.8 tok/s	29454 ms	4K
RAG	F	Too heavy	5.8 tok/s	60207 ms	4K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	7.4 GB	Low	C51
Q3_K_S	3	9.3 GB	Low	F0

Get started

Copy-paste commands to run Codestral RAG 19B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-rag-19b-pruned-i1-gguf && lms server start

Upgrade options

Hardware that runs Codestral RAG 19B Pruned i1 well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.17 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.12.6 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.13.1 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

RTX 4090 24GBBiggest leap

24 GB VRAM (+13)1008 GB/s (+524)

Makes the model fit on the accelerator instead of staying completely out of reach.66.1 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.

~$1,599 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run Codestral RAG 19B Pruned i1?

Yes, GTX 1080 Ti 11GB can run Codestral RAG 19B Pruned i1 at Q2_K quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 16.1 GB which exceeds available memory, but at Q2_K it needs only 11.9 GB. Expected decode speed: 20.0 tok/s.

How much VRAM does Codestral RAG 19B Pruned i1 need?

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 16.1 GB at Q4_K_M quantization. On GTX 1080 Ti 11GB, it fits at Q2_K using 11.9 GB.

What is the best quantization for Codestral RAG 19B Pruned i1?

The recommended quantization is Q4_K_M, but on GTX 1080 Ti 11GB the best fitting quantization is Q2_K, which uses 11.9 GB.

What speed will Codestral RAG 19B Pruned i1 run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Codestral RAG 19B Pruned i1 achieves approximately 20.0 tokens per second decode speed with a time-to-first-token of 9695ms using Q2_K quantization.

Can GTX 1080 Ti 11GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on GTX 1080 Ti 11GB receives a F grade with 7.8 tok/s and 4K context.

What context window can Codestral RAG 19B Pruned i1 use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Codestral RAG 19B Pruned i1 can safely use up to 9K tokens of context at Q2_K quantization. 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 Codestral RAG 19B Pruned i1

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