Can Codestral 2 25.08 run on RTX 3080 10GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Codestral 2 25.08 needs ~17.8 GB but RTX 3080 10GB only has 10.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: MediumStack: StandardBottleneck: 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) — 17.8 GB, exceeds 10.0 GB available

17.8 GB required10.0 GB available

178% VRAM needed

7.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.5 tok/s

TTFT

25662 ms

Safe context

Memory

17.8 GB / 10.0 GB

Offload

40%

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral 2 25.08 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: 7.5 tok/s decode · 25.7s TTFT (warm) · 19 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 17.8 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	8.8 tok/s	12049 ms	4K
Coding	F	Too heavy	7.5 tok/s	25662 ms	4K
Agentic Coding	F	Too heavy	5.8 tok/s	48952 ms	4K
Reasoning	F	Too heavy	7.5 tok/s	30328 ms	4K
RAG	F	Too heavy	5.8 tok/s	61189 ms	4K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on RTX 3080 10GB (10.0 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

升级选项

能流畅运行 Codestral 2 25.08 的硬件

RTX 5060 Ti 16GB最佳性价比

16 GB VRAM (+6)

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.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA 升级

16 GB VRAM (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.6.4 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.

~$499 MSRP

RTX 4000 Ada 20GB经济之选

20 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.20.1 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

RTX 5090 Laptop 24GB最大飞跃

24 GB VRAM (+14)896 GB/s (+136)

Makes the model fit on the accelerator instead of staying completely out of reach.53.8 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.

Frequently asked questions

Can RTX 3080 10GB run Codestral 2 25.08?

No, Codestral 2 25.08 requires more memory than RTX 3080 10GB provides.

How much VRAM does Codestral 2 25.08 need?

Codestral 2 25.08 (22B parameters) requires approximately 17.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 2 25.08?

The recommended quantization for Codestral 2 25.08 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 2 25.08 run at on RTX 3080 10GB?

On RTX 3080 10GB, Codestral 2 25.08 achieves approximately 7.5 tokens per second decode speed with a time-to-first-token of 25662ms using Q4_K_M quantization.

Can RTX 3080 10GB run Codestral 2 25.08 for coding?

For coding workloads, Codestral 2 25.08 on RTX 3080 10GB receives a F grade with 7.5 tok/s and 4K context.

What context window can Codestral 2 25.08 use on RTX 3080 10GB?

On RTX 3080 10GB, Codestral 2 25.08 can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 2 25.08 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 10GB See all hardware for Codestral 2 25.08

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