Can Codestral 22B v0.1 IMat run on RTX 2060 Super 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Codestral 22B v0.1 IMat needs ~18.0 GB but RTX 2060 Super 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) — 18.0 GB, exceeds 8.0 GB available

18.0 GB required8.0 GB available

225% VRAM needed

10.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.9 tok/s

TTFT

66659 ms

Safe context

Memory

18.0 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights13.4 GB

KV Cache2.6 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 v0.1 IMat on RTX 2060 Super 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: 2.9 tok/s decode · 66.7s TTFT (warm) · 7 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 18.0 GB, but this setup only exposes 8.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	2.9 tok/s	36360 ms	4K
Coding	F	Too heavy	2.9 tok/s	66659 ms	4K
Agentic Coding	F	Too heavy	2.9 tok/s	96959 ms	4K
Reasoning	F	Too heavy	2.9 tok/s	78779 ms	4K
RAG	F	Too heavy	2.9 tok/s	121199 ms	4K

Quantization options

How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on RTX 2060 Super 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	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-Optionen

Hardware, die Codestral 22B v0.1 IMat gut ausführt

RTX 5060 Ti 16GBBestes Preis-Leistungs-Verhältnis

16 GB VRAM (+8)

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

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

Raises estimated decode speed by about 293%.

ca. $449 MSRP

RTX 4060 Ti 16GBNVIDIA-Upgrade

16 GB VRAM (+8)

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

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

Raises estimated decode speed by about 190%.

ca. $499 MSRP

RTX 4000 Ada 20GBGünstige Wahl

20 GB VRAM (+12)

Makes the model fit on the accelerator instead of staying completely out of reach.20.9 tok/s Dekodierung

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.

ca. $1,250 MSRP

RTX 5090 32GBGrößter Sprung

32 GB VRAM (+24)1792 GB/s (+1344)

Makes the model fit on the accelerator instead of staying completely out of reach.89.5 tok/s Dekodierung

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.

ca. $1,999 MSRP

Frequently asked questions

Can RTX 2060 Super 8GB run Codestral 22B v0.1 IMat?

No, Codestral 22B v0.1 IMat requires more memory than RTX 2060 Super 8GB provides.

How much VRAM does Codestral 22B v0.1 IMat need?

Codestral 22B v0.1 IMat (22B parameters) requires approximately 18.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1 IMat?

The recommended quantization for Codestral 22B v0.1 IMat is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 IMat run at on RTX 2060 Super 8GB?

On RTX 2060 Super 8GB, Codestral 22B v0.1 IMat achieves approximately 2.9 tokens per second decode speed with a time-to-first-token of 66659ms using Q4_K_M quantization.

Can RTX 2060 Super 8GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on RTX 2060 Super 8GB receives a F grade with 2.9 tok/s and 4K context.

What context window can Codestral 22B v0.1 IMat use on RTX 2060 Super 8GB?

On RTX 2060 Super 8GB, Codestral 22B v0.1 IMat can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 22B v0.1 IMat feels slow on RTX 2060 Super 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 2060 Super 8GB See all hardware for Codestral 22B v0.1 IMat

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