Can Codestral 22B run on RTX 4060 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Codestral 22B needs ~17.9 GB but RTX 4060 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: 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) — 17.9 GB, exceeds 8.0 GB available

17.9 GB required8.0 GB available

224% VRAM needed

9.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.4 tok/s

TTFT

81146 ms

Safe context

Memory

17.9 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral 22B on RTX 4060 8GB
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: 2.4 tok/s decode · 81.1s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 17.9 GB, but this setup only exposes 8.0 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	2.6 tok/s	41363 ms	4K
Coding	F	Too heavy	2.2 tok/s	87232 ms	4K
Agentic Coding	F	Too heavy	2.4 tok/s	118030 ms	4K
Reasoning	F	Too heavy	2.4 tok/s	95900 ms	4K
RAG	F	Too heavy	2.4 tok/s	147538 ms	4K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on RTX 4060 8GB (8.0 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

Upgrade options

Hardware that runs Codestral 22B well

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+176)

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.

Raises estimated decode speed by about 417%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)288 GB/s (+16)

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.

Raises estimated decode speed by about 279%.

~$499 MSRP

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+12)360 GB/s (+88)

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

RTX 4090 24GBBiggest leap

24 GB VRAM (+16)1008 GB/s (+736)

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

Can RTX 4060 8GB run Codestral 22B?

No, Codestral 22B requires more memory than RTX 4060 8GB provides.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 17.9 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 4060 8GB?

On RTX 4060 8GB, Codestral 22B achieves approximately 2.2 tokens per second decode speed with a time-to-first-token of 87232ms using Q4_K_M quantization.

Can RTX 4060 8GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on RTX 4060 8GB receives a F grade with 2.2 tok/s and 4K context.

What context window can Codestral 22B use on RTX 4060 8GB?

On RTX 4060 8GB, 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 4060 8GB?

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 4060 8GB See all hardware for Codestral 22B

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