Can Codestral 22B run on NVIDIA L4 24GB?

YES — Runs Great

B61Good

Estimated from fit model

Codestral 22B needs ~19.5 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced

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) — 19.5 GB, 15.6 tok/s, Runs well

19.5 GB required24.0 GB available

81% VRAM used

Fit status

Runs well

Decode

15.6 tok/s

TTFT

12394 ms

Safe context

33K

Memory

19.5 GB / 24.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral 22B on NVIDIA L4 24GB
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: 15.6 tok/s decode · 12.4s TTFT (warm) · 39 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	15.6 tok/s	6760 ms	33K
Coding	B	Runs well	15.6 tok/s	12394 ms	33K
Agentic Coding	B	Tight fit	15.6 tok/s	18028 ms	33K
Reasoning	B	Runs well	15.6 tok/s	14648 ms	33K
RAG	B	Tight fit	15.6 tok/s	22535 ms	33K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	B58
Q3_K_S	3	10.8 GB	Low	B60
NVFP4	4	12.3 GB	Medium	B60
Q4_K_M	4	13.4 GB	Medium	B60
Q5_K_M	5	15.8 GB	High	B60
Q6_KBest for your GPU	6	18.0 GB	High	B59
Q8_0	8	23.5 GB	Very High	F0
F16	16	45.1 GB	Maximum	F0

Get started

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

Run

ollama run codestral

Opções de upgrade

Hardware que roda bem Codestral 22B

RTX 5090 32GBOpção econômica

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 517%.96.2 tok/s decodificação

Raises estimated decode speed by about 517%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMelhor custo-benefício

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 287%.60.3 tok/s decodificação

Raises estimated decode speed by about 287%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

RTX 5000 Ada 32GBUpgrade NVIDIA

32 GB VRAM (+8)576 GB/s (+276)

Raises estimated decode speed by about 137%.36.9 tok/s decodificação

Raises estimated decode speed by about 137%.

Adds memory headroom for longer context windows and future model growth.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Codestral 22B?

Yes, NVIDIA L4 24GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 15.6 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 19.5 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Codestral 22B achieves approximately 15.6 tokens per second decode speed with a time-to-first-token of 12394ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on NVIDIA L4 24GB receives a B grade with 15.6 tok/s and 33K context.

What context window can Codestral 22B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Codestral 22B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for Codestral 22B

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