Can Codestral 22B v0.1 IMat run on NVIDIA L4 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

Codestral 22B v0.1 IMat needs ~19.6 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~15 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) — 19.6 GB, 14.5 tok/s, Runs well

19.6 GB required24.0 GB available

82% VRAM used

Fit status

Runs well

Decode

14.5 tok/s

TTFT

13324 ms

Safe context

43K

Memory

19.6 GB / 24.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 IMat 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: 14.5 tok/s decode · 13.3s TTFT (warm) · 36 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	C	Runs well	14.5 tok/s	7267 ms	43K
Coding	C	Runs well	14.5 tok/s	13324 ms	43K
Agentic Coding	C	Tight fit	14.5 tok/s	19380 ms	43K
Reasoning	C	Runs well	14.5 tok/s	15746 ms	43K
RAG	C	Tight fit	14.5 tok/s	24225 ms	43K

Quantization options

How Codestral 22B v0.1 IMat (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	C48
Q3_K_S	3	10.8 GB	Low	C49
NVFP4	4

Get started

Copy-paste commands to run Codestral 22B v0.1 IMat on your machine.

Run

lms load hf-legraphista--codestral-22b-v0-1-imat-gguf && lms server start

Upgrade options

Hardware that runs Codestral 22B v0.1 IMat well

RTX 5090 32GBBudget pick

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

Raises estimated decode speed by about 517%.89.5 tok/s decode

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 32GBBest value

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

Raises estimated decode speed by about 287%.56.1 tok/s decode

Raises estimated decode speed by about 287%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

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

Raises estimated decode speed by about 137%.34.3 tok/s decode

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 v0.1 IMat?

Yes, NVIDIA L4 24GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 14.5 tok/s.

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

Codestral 22B v0.1 IMat (22B parameters) requires approximately 19.6 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Codestral 22B v0.1 IMat achieves approximately 14.5 tokens per second decode speed with a time-to-first-token of 13324ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on NVIDIA L4 24GB receives a C grade with 14.5 tok/s and 43K context.

What context window can Codestral 22B v0.1 IMat use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Codestral 22B v0.1 IMat can safely use up to 43K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for Codestral 22B v0.1 IMat

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