Can Codestral 2 25.08 run on NVIDIA L40 48GB?

YES — Runs Great

A85Great

Estimated from fit model

Codestral 2 25.08 needs ~23.3 GB VRAM. NVIDIA L40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: SGLangCapacity: RoomyBandwidth: HighStack: OptimizedBottleneck: 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) — 23.3 GB, 54.0 tok/s, Runs well

23.3 GB required48.0 GB available

49% VRAM used

Fit status

Runs well

Decode

54.0 tok/s

TTFT

3586 ms

Safe context

178K

Memory

23.3 GB / 48.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime2.6 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodestral 2 25.08 on NVIDIA L40 48GB
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: 54.0 tok/s decode · 3.6s TTFT (warm) · 135 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	A	Runs well	54.0 tok/s	1956 ms	178K
Coding	A	Runs well	54.0 tok/s	3586 ms	178K
Agentic Coding	S	Runs well	54.0 tok/s	5216 ms	178K
Reasoning	A	Runs well	54.0 tok/s	4238 ms	178K
RAG	S	Runs well	54.0 tok/s	6520 ms	178K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on NVIDIA L40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	A77
Q3_K_S	3	10.8 GB	Low	A77
NVFP4	4

Get started

Copy-paste commands to run Codestral 2 25.08 on your machine.

Run

lms load codestral-2508 && lms server start

Your hardware

More models your NVIDIA L40 48GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	101.9 tok/s
Qwen 3.5 27B	27B	S	44.2 tok/s

Frequently asked questions

Can NVIDIA L40 48GB run Codestral 2 25.08?

Yes, NVIDIA L40 48GB can run Codestral 2 25.08 with a A grade (Runs well). Expected decode speed: 54.0 tok/s.

How much VRAM does Codestral 2 25.08 need?

Codestral 2 25.08 (22B parameters) requires approximately 23.3 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 NVIDIA L40 48GB?

On NVIDIA L40 48GB, Codestral 2 25.08 achieves approximately 54.0 tokens per second decode speed with a time-to-first-token of 3586ms using Q4_K_M quantization.

Can NVIDIA L40 48GB run Codestral 2 25.08 for coding?

For coding workloads, Codestral 2 25.08 on NVIDIA L40 48GB receives a A grade with 54.0 tok/s and 178K context.

What context window can Codestral 2 25.08 use on NVIDIA L40 48GB?

On NVIDIA L40 48GB, Codestral 2 25.08 can safely use up to 178K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA L40 48GB See all hardware for Codestral 2 25.08

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