Can Codestral 22B run on RTX 5070 12GB?

YES — With Q2_K

C50Usable

○Estimated from fit model

Codestral 22B needs ~13.4 GB VRAM. RTX 5070 12GB has 12.0 GB. With Q2_K quantization, expect ~27 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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 5070 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

11.0 tok/s

TTFT

17581 ms

Safe context

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 5070 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: 11.0 tok/s decode · 17.6s TTFT (warm) · 28 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	11.8 tok/s	8952 ms	4K
Coding	F	Too heavy	10.2 tok/s	18900 ms	4K
Agentic Coding	F	Too heavy	7.9 tok/s	35510 ms	4K
Reasoning	F	Too heavy	10.2 tok/s	22336 ms	4K
RAG	F	Too heavy	7.9 tok/s	44388 ms	4K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on RTX 5070 12GB (12.0 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

Get started

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

Run

ollama run codestral

Upgrade options

Hardware that runs Codestral 22B well

👁 NVIDIA
RTX 5060 Ti 16GBBest value

16 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.12.4 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 (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.9.1 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 (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.22.5 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.

~$1,250 MSRP

👁 NVIDIA
RTX 4090 24GBBiggest leap

24 GB VRAM (+12)1008 GB/s (+336)

Makes the model fit on the accelerator instead of staying completely out of reach.61.4 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.

~$1,599 MSRP

Frequently asked questions

See all results for RTX 5070 12GB See all hardware for Codestral 22B

URL: https://willitrunai.com/can-run/codestral-22b-on-rtx-5070-12gb

⇱

Can Codestral 22B run on RTX 5070 12GB?

YES — With Q2_K

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

Get started

Hardware that runs Codestral 22B well

Frequently asked questions