Can Codestral 22B v0.1 run on RTX 3080 10GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Codestral 22B v0.1 needs ~18.2 GB but RTX 3080 10GB only has 10.0 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) — 18.2 GB, exceeds 10.0 GB available

18.2 GB required10.0 GB available

182% VRAM needed

8.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.2 tok/s

TTFT

21151 ms

Safe context

Memory

18.2 GB / 10.0 GB

Offload

50%

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral 22B v0.1 on RTX 3080 10GB
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: 9.2 tok/s decode · 21.2s TTFT (warm) · 23 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 18.2 GB, but this setup only exposes 10.0 GB of usable VRAM.

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	10.7 tok/s	9884 ms	4K
Coding	F	Too heavy	9.2 tok/s	21151 ms	4K
Agentic Coding	F	Too heavy	6.9 tok/s	40661 ms	4K
Reasoning	F	Too heavy	9.2 tok/s	24996 ms	4K
RAG	F	Too heavy	6.9 tok/s	50826 ms	4K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on RTX 3080 10GB (10.0 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

Upgrade options

Hardware that runs Codestral 22B v0.1 well

👁 NVIDIA
RTX 5060 Ti 16GBBest value

16 GB VRAM (+6)

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

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

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

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+6)

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

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

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

~$499 MSRP

👁 NVIDIA
RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.20.9 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.

~$1,250 MSRP

👁 NVIDIA
RTX 5090 32GBBiggest leap

32 GB VRAM (+22)1792 GB/s (+1032)

Makes the model fit on the accelerator instead of staying completely out of reach.89.5 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.

~$1,999 MSRP

Frequently asked questions

See all results for RTX 3080 10GB See all hardware for Codestral 22B v0.1

URL: https://willitrunai.com/can-run/hf-lmstudio-community--codestral-22b-v0-1-gguf-on-rtx-3080-10gb

⇱ Codestral 22B v0.1 on RTX 3080 10GB? No — Alternatives

Can Codestral 22B v0.1 run on RTX 3080 10GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs Codestral 22B v0.1 well

Frequently asked questions