Will It Run AI

Can Codestral 21B Pruned i1 run on GTX 1080 Ti 11GB?

YES — With Q2_K

D32Poor
Estimated from fit model

Codestral 21B Pruned i1 needs ~13.0 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q2_K quantization, expect ~15 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload
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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 21B Pruned i1 at Q4_K_M needs 17.6 GB — too much for GTX 1080 Ti 11GB (11.0 GB). Runs at Q2_K (13.0 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 17.6 GB, exceeds 11.0 GB available
17.6 GB required11.0 GB available
160% VRAM needed

6.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.8 tok/s

TTFT

33301 ms

Safe context

4K

Memory

17.6 GB / 11.0 GB

Offload

40%

Memory breakdown

Weights12.8 GB
KV Cache2.5 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 21B 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: 5.8 tok/s decode · 33.3s TTFT (warm) · 15 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 1.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy6.8 tok/s15487 ms4K
CodingFToo heavy5.8 tok/s33301 ms4K
Agentic CodingFToo heavy4.4 tok/s64594 ms4K
ReasoningFToo heavy5.8 tok/s39356 ms4K
RAGFToo heavy4.4 tok/s80743 ms4K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
8.2 GB
LowF0
Q3_K_S
3
10.3 GB
LowF0
NVFP4
4
11.8 GB
MediumF0
Q4_K_M
4
12.8 GB
MediumF0
Q5_K_M
5
15.1 GB
HighF0
Q6_K
6
17.2 GB
HighF0
Q8_0
8
22.5 GB
Very HighF0
F16
16
43.1 GB
MaximumF0

Get started

Copy-paste commands to run Codestral 21B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-21b-pruned-i1-gguf && lms server start

Opções de upgrade

Hardware que roda bem Codestral 21B Pruned i1

NVIDIARTX 5060 Ti 16GBMelhor custo-benefício
16 GB VRAM (+5)
D
Makes the model fit on the accelerator instead of staying completely out of reach.12.9 tok/s decodificação

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

Raises estimated decode speed by about 122%.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBUpgrade NVIDIA
16 GB VRAM (+5)
D
Makes the model fit on the accelerator instead of staying completely out of reach.9.5 tok/s decodificação

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

Raises estimated decode speed by about 64%.

~$499 MSRP

NVIDIARTX 4000 Ada 20GBOpção econômica
20 GB VRAM (+9)
C
Makes the model fit on the accelerator instead of staying completely out of reach.21.9 tok/s decodificação

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.

~$1,250 MSRP

NVIDIARTX 4090 24GBMaior salto
24 GB VRAM (+13)1008 GB/s (+524)
B
Makes the model fit on the accelerator instead of staying completely out of reach.59.8 tok/s decodificação

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 21B Pruned i1?

Yes, GTX 1080 Ti 11GB can run Codestral 21B Pruned i1 at Q2_K quantization (Very compromised (needs ~1.2 GB host RAM)). The recommended Q4_K_M requires 17.6 GB which exceeds available memory, but at Q2_K it needs only 13.0 GB. Expected decode speed: 15.1 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 17.6 GB at Q4_K_M quantization. On GTX 1080 Ti 11GB, it fits at Q2_K using 13.0 GB.

What is the best quantization for Codestral 21B Pruned i1?

The recommended quantization is Q4_K_M, but on GTX 1080 Ti 11GB the best fitting quantization is Q2_K, which uses 13.0 GB.

What speed will Codestral 21B Pruned i1 run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Codestral 21B Pruned i1 achieves approximately 15.1 tokens per second decode speed with a time-to-first-token of 12818ms using Q2_K quantization.

Can GTX 1080 Ti 11GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on GTX 1080 Ti 11GB receives a F grade with 5.8 tok/s and 4K context.

What context window can Codestral 21B Pruned i1 use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Codestral 21B Pruned i1 can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 21B 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.

See all results for GTX 1080 Ti 11GBSee all hardware for Codestral 21B Pruned i1
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