Can Codestral 22B v0.1 IMat run on NVIDIA L20 48GB?

YES — Runs Great

C49Usable

Estimated from fit model

Codestral 22B v0.1 IMat needs ~22.0 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~47 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 22.0 GB, 47.0 tok/s, Runs well

22.0 GB required48.0 GB available

46% VRAM used

Fit status

Runs well

Decode

47.0 tok/s

TTFT

4119 ms

Safe context

177K

Memory

22.0 GB / 48.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 IMat on NVIDIA L20 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: 47.0 tok/s decode · 4.1s TTFT (warm) · 118 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	47.0 tok/s	2247 ms	177K
Coding	C	Runs well	47.0 tok/s	4119 ms	177K
Agentic Coding	C	Runs well	47.0 tok/s	5992 ms	177K
Reasoning	C	Runs well	47.0 tok/s	4868 ms	177K
RAG	C	Runs well	47.0 tok/s	7490 ms	177K

Quantization options

How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C42
Q3_K_S	3	10.8 GB	Low	C43
NVFP4	4	12.3 GB	Medium	C43
Q4_K_M	4	13.4 GB	Medium	C44
Q5_K_M	5	15.8 GB	High	C44
Q6_K	6	18.0 GB	High	C45
Q8_0Best for your GPU	8	23.5 GB	Very High	C47
F16	16	45.1 GB	Maximum	F0

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

Frequently asked questions

Can NVIDIA L20 48GB run Codestral 22B v0.1 IMat?

Yes, NVIDIA L20 48GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 47.0 tok/s.

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

Codestral 22B v0.1 IMat (22B parameters) requires approximately 22.0 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 NVIDIA L20 48GB?

On NVIDIA L20 48GB, Codestral 22B v0.1 IMat achieves approximately 47.0 tokens per second decode speed with a time-to-first-token of 4119ms using Q4_K_M quantization.

Can NVIDIA L20 48GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on NVIDIA L20 48GB receives a C grade with 47.0 tok/s and 177K context.

What context window can Codestral 22B v0.1 IMat use on NVIDIA L20 48GB?

On NVIDIA L20 48GB, Codestral 22B v0.1 IMat can safely use up to 177K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L20 48GB See all hardware for Codestral 22B v0.1 IMat

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