Can Codestral 22B v0.1 run on NVIDIA A10 24GB?

YES — Runs Great

C54Usable

Estimated from fit model

Codestral 22B v0.1 needs ~19.6 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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, 34.9 tok/s, Runs well

19.6 GB required24.0 GB available

82% VRAM used

Fit status

Runs well

Decode

34.9 tok/s

TTFT

5552 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 on NVIDIA A10 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: 34.9 tok/s decode · 5.6s TTFT (warm) · 87 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	34.9 tok/s	3028 ms	43K
Coding	C	Runs well	34.9 tok/s	5552 ms	43K
Agentic Coding	C	Tight fit	34.9 tok/s	8075 ms	43K
Reasoning	C	Runs well	34.9 tok/s	6561 ms	43K
RAG	C	Tight fit	34.9 tok/s	10094 ms	43K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on NVIDIA A10 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	12.3 GB	Medium	C50
Q4_K_M	4	13.4 GB	Medium	C50
Q5_K_M	5	15.8 GB	High	C50
Q6_KBest for your GPU	6	18.0 GB	High	C49
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 v0.1 on your machine.

Run

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

Upgrade-Optionen

Hardware, die Codestral 22B v0.1 gut ausführt

RTX 5090 32GBGünstige Wahl

32 GB VRAM (+8)1792 GB/s (+1192)

Raises estimated decode speed by about 156%.89.5 tok/s Dekodierung

Raises estimated decode speed by about 156%.

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

ca. $1,999 MSRP

RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis

32 GB VRAM (+8)896 GB/s (+296)

Raises estimated decode speed by about 61%.56.1 tok/s Dekodierung

Raises estimated decode speed by about 61%.

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

ca. $2,499 MSRP

Frequently asked questions

Can NVIDIA A10 24GB run Codestral 22B v0.1?

Yes, NVIDIA A10 24GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 34.9 tok/s.

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

Codestral 22B v0.1 (22B parameters) requires approximately 19.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1?

The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 run at on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Codestral 22B v0.1 achieves approximately 34.9 tokens per second decode speed with a time-to-first-token of 5552ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on NVIDIA A10 24GB receives a C grade with 34.9 tok/s and 43K context.

What context window can Codestral 22B v0.1 use on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Codestral 22B v0.1 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 A10 24GB See all hardware for Codestral 22B v0.1

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