Can Codestral 22B v0.1 run on GTX 1080 Ti 11GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Codestral 22B v0.1 needs ~18.3 GB but GTX 1080 Ti 11GB only has 11.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity

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) — 18.3 GB, exceeds 11.0 GB available

18.3 GB required11.0 GB available

166% VRAM needed

7.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.1 tok/s

TTFT

38136 ms

Safe context

Memory

18.3 GB / 11.0 GB

Offload

40%

Memory breakdown

Weights13.4 GB

KV Cache2.6 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 22B v0.1 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: 5.1 tok/s decode · 38.1s TTFT (warm) · 13 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 18.3 GB, but this setup only exposes 11.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	6.0 tok/s	17718 ms	4K
Coding	F	Too heavy	5.1 tok/s	38136 ms	4K
Agentic Coding	F	Too heavy	3.8 tok/s	74092 ms	4K
Reasoning	F	Too heavy	5.1 tok/s	45070 ms	4K
RAG	F	Too heavy	3.8 tok/s	92615 ms	4K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	F0
Q3_K_S	3	10.8 GB	Low	F0
NVFP4	4	12.3 GB	Medium	F0
Q4_K_M	4	13.4 GB	Medium	F0
Q5_K_M	5	15.8 GB	High	F0
Q6_K	6	18.0 GB	High	F0
Q8_0	8	23.5 GB	Very High	F0
F16	16	45.1 GB	Maximum	F0

升级选项

能流畅运行 Codestral 22B v0.1 的硬件

RTX 5060 Ti 16GB最佳性价比

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.11.4 tok/s 解码

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

Raises estimated decode speed by about 124%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA 升级

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.8.4 tok/s 解码

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

Raises estimated decode speed by about 65%.

~$499 MSRP

RTX 4000 Ada 20GB经济之选

20 GB VRAM (+9)

Makes the model fit on the accelerator instead of staying completely out of reach.20.9 tok/s 解码

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

RTX 5090 32GB最大飞跃

32 GB VRAM (+21)1792 GB/s (+1308)

Makes the model fit on the accelerator instead of staying completely out of reach.89.5 tok/s 解码

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.

~$1,999 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run Codestral 22B v0.1?

No, Codestral 22B v0.1 requires more memory than GTX 1080 Ti 11GB provides.

How much VRAM does Codestral 22B v0.1 need?

Codestral 22B v0.1 (22B parameters) requires approximately 18.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1?

The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Codestral 22B v0.1 achieves approximately 5.1 tokens per second decode speed with a time-to-first-token of 38136ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on GTX 1080 Ti 11GB receives a F grade with 5.1 tok/s and 4K context.

What context window can Codestral 22B v0.1 use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Codestral 22B v0.1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 22B v0.1 feels slow on GTX 1080 Ti 11GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1080 Ti 11GB See all hardware for Codestral 22B v0.1

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