Can Codestral 22B v0.1 run on RTX A5000 24GB?

YES — Runs Great

C54Usable

Estimated from fit model

Codestral 22B v0.1 needs ~19.6 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~40 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, 40.1 tok/s, Runs well

19.6 GB required24.0 GB available

82% VRAM used

Fit status

Runs well

Decode

40.1 tok/s

TTFT

4833 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 RTX A5000 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: 40.1 tok/s decode · 4.8s TTFT (warm) · 100 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	40.1 tok/s	2636 ms	43K
Coding	C	Runs well	40.1 tok/s	4833 ms	43K
Agentic Coding	C	Tight fit	40.1 tok/s	7030 ms	43K
Reasoning	C	Runs well	40.1 tok/s	5711 ms	43K
RAG	C	Tight fit	40.1 tok/s	8787 ms	43K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on RTX A5000 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 on your machine.

Run

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

Upgrade options

Hardware that runs Codestral 22B v0.1 well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1024)

Raises estimated decode speed by about 123%.89.5 tok/s decode

Raises estimated decode speed by about 123%.

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

~$1,999 MSRP

Frequently asked questions

Can RTX A5000 24GB run Codestral 22B v0.1?

Yes, RTX A5000 24GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 40.1 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 RTX A5000 24GB?

On RTX A5000 24GB, Codestral 22B v0.1 achieves approximately 40.1 tokens per second decode speed with a time-to-first-token of 4833ms using Q4_K_M quantization.

Can RTX A5000 24GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on RTX A5000 24GB receives a C grade with 40.1 tok/s and 43K context.

What context window can Codestral 22B v0.1 use on RTX A5000 24GB?

On RTX A5000 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 RTX A5000 24GB See all hardware for Codestral 22B v0.1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-bartowski--codestral-22b-v0-1-gguf-on-a5000-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: