Will It Run AI

Can Codestral 22B run on RTX 3080 Ti 12GB?

YES — With Q2_K

C51Usable
Estimated from fit model

Codestral 22B needs ~13.4 GB VRAM. RTX 3080 Ti 12GB has 12.0 GB. With Q2_K quantization, expect ~43 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: 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 22B at Q4_K_M needs 18.3 GB — too much for RTX 3080 Ti 12GB (12.0 GB). Runs at Q2_K (13.4 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 18.3 GB, exceeds 12.0 GB available
18.3 GB required12.0 GB available
153% VRAM needed

6.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

16.8 tok/s

TTFT

11556 ms

Safe context

4K

Memory

18.3 GB / 12.0 GB

Offload

30%

Memory breakdown

Weights13.4 GB
KV Cache2.4 GB
Runtime1.2 GB
Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsCodestral 22B on RTX 3080 Ti 12GB
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: 16.8 tok/s decode · 11.6s TTFT (warm) · 42 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.

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 0.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy19.4 tok/s5449 ms4K
CodingFToo heavy16.8 tok/s11556 ms4K
Agentic CodingFToo heavy12.9 tok/s21890 ms4K
ReasoningFToo heavy16.8 tok/s13657 ms4K
RAGFToo heavy12.9 tok/s27363 ms4K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on RTX 3080 Ti 12GB (12.0 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

Get started

Copy-paste commands to run Codestral 22B on your machine.

Run

ollama run codestral

アップグレードオプション

Codestral 22Bを快適に動かすハードウェア

NVIDIARTX 5060 Ti 16GBコスパ最良
16 GB VRAM (+4)
C
Makes the model fit on the accelerator instead of staying completely out of reach.12.4 tok/sデコード

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アップグレード
16 GB VRAM (+4)
C
Makes the model fit on the accelerator instead of staying completely out of reach.9.1 tok/sデコード

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コスパ重視
20 GB VRAM (+8)
B
Makes the model fit on the accelerator instead of staying completely out of reach.22.5 tok/sデコード

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最大の飛躍
24 GB VRAM (+12)1008 GB/s (+96)
B
Makes the model fit on the accelerator instead of staying completely out of reach.61.4 tok/sデコード

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 Ti 12GB run Codestral 22B?

Yes, RTX 3080 Ti 12GB can run Codestral 22B at Q2_K quantization (Very compromised (needs ~0.9 GB host RAM)). The recommended Q4_K_M requires 18.3 GB which exceeds available memory, but at Q2_K it needs only 13.4 GB. Expected decode speed: 42.6 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 18.3 GB at Q4_K_M quantization. On RTX 3080 Ti 12GB, it fits at Q2_K using 13.4 GB.

What is the best quantization for Codestral 22B?

The recommended quantization is Q4_K_M, but on RTX 3080 Ti 12GB the best fitting quantization is Q2_K, which uses 13.4 GB.

What speed will Codestral 22B run at on RTX 3080 Ti 12GB?

On RTX 3080 Ti 12GB, Codestral 22B achieves approximately 42.6 tokens per second decode speed with a time-to-first-token of 4546ms using Q2_K quantization.

Can RTX 3080 Ti 12GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on RTX 3080 Ti 12GB receives a F grade with 16.8 tok/s and 4K context.

What context window can Codestral 22B use on RTX 3080 Ti 12GB?

On RTX 3080 Ti 12GB, Codestral 22B can safely use up to 7K tokens of context at Q2_K quantization. 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 Ti 12GB?

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 RTX 3080 Ti 12GBSee all hardware for Codestral 22B
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