Will It Run AI

Can Codestral 22B run on RTX 3080 10GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

Codestral 22B needs ~18.1 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
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 18.1 GB, exceeds 10.0 GB available
18.1 GB required10.0 GB available
181% VRAM needed

8.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.0 tok/s

TTFT

19365 ms

Safe context

4K

Memory

18.1 GB / 10.0 GB

Offload

40%

Memory breakdown

Weights13.4 GB
KV Cache2.4 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 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: 10.0 tok/s decode · 19.4s TTFT (warm) · 25 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 18.1 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

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy11.6 tok/s9116 ms4K
CodingFToo heavy10.0 tok/s19365 ms4K
Agentic CodingFToo heavy7.7 tok/s36784 ms4K
ReasoningFToo heavy10.0 tok/s22886 ms4K
RAGFToo heavy7.7 tok/s45980 ms4K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
8.6 GB
LowF0
Q3_K_S
3
10.8 GB
LowF0
NVFP4
4
12.3 GB
MediumF0
Q4_K_M
4
13.4 GB
MediumF0
Q5_K_M
5
15.8 GB
HighF0
Q6_K
6
18.0 GB
HighF0
Q8_0
8
23.5 GB
Very HighF0
F16
16
45.1 GB
MaximumF0

升级选项

能流畅运行 Codestral 22B 的硬件

NVIDIARTX 5060 Ti 16GB最佳性价比
16 GB VRAM (+6)
C
Makes the model fit on the accelerator instead of staying completely out of reach.12.4 tok/s 解码

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

NVIDIARTX 4060 Ti 16GBNVIDIA 升级
16 GB VRAM (+6)
C
Makes the model fit on the accelerator instead of staying completely out of reach.9.1 tok/s 解码

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

NVIDIARTX 4000 Ada 20GB经济之选
20 GB VRAM (+10)
B
Makes the model fit on the accelerator instead of staying completely out of reach.22.5 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.

~$1,250 MSRP

NVIDIARTX 4090 24GB最大飞跃
24 GB VRAM (+14)1008 GB/s (+248)
B
Makes the model fit on the accelerator instead of staying completely out of reach.61.4 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.

~$1,599 MSRP

Frequently asked questions

Can RTX 3080 10GB run Codestral 22B?

No, Codestral 22B requires more memory than RTX 3080 10GB provides.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 18.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B?

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

What speed will Codestral 22B run at on RTX 3080 10GB?

On RTX 3080 10GB, Codestral 22B achieves approximately 10.0 tokens per second decode speed with a time-to-first-token of 19365ms using Q4_K_M quantization.

Can RTX 3080 10GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on RTX 3080 10GB receives a F grade with 10.0 tok/s and 4K context.

What context window can Codestral 22B use on RTX 3080 10GB?

On RTX 3080 10GB, Codestral 22B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 22B feels slow on RTX 3080 10GB?

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 RTX 3080 10GBSee all hardware for Codestral 22B
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