Can Codestral 2 25.08 run on RTX A5000 24GB?

YES — Runs Great

S88Excellent

○Estimated from fit model

Codestral 2 25.08 needs ~19.2 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~39 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 19.2 GB, 38.5 tok/s, Runs well

19.2 GB required24.0 GB available

80% VRAM used

Fit status

Runs well

Decode

38.5 tok/s

TTFT

5034 ms

Safe context

48K

Memory

19.2 GB / 24.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral 2 25.08 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: 38.5 tok/s decode · 5.0s TTFT (warm) · 96 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	S	Runs well	38.5 tok/s	2746 ms	48K
Coding	S	Runs well	38.5 tok/s	5034 ms	48K
Agentic Coding	S	Tight fit	38.5 tok/s	7323 ms	48K
Reasoning	S	Runs well	38.5 tok/s	5950 ms	48K
RAG	S	Tight fit	38.5 tok/s	9153 ms	48K

Quantization options

How Codestral 2 25.08 (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	A82
Q3_K_S	3	10.8 GB	Low	A84
NVFP4	4	12.3 GB	Medium	A85
Q4_K_M	4	13.4 GB	Medium	A84
Q5_K_M	5	15.8 GB	High	A84
Q6_KBest for your GPU	6	18.0 GB	High	A84
Q8_0	8	23.5 GB	Very High	F0
F16	16	45.1 GB	Maximum	F0

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 RTX A5000 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	81.3 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	35.3 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	26.8 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	A	35 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	84.1 tok/s

Frequently asked questions

See all results for RTX A5000 24GB See all hardware for Codestral 2 25.08

URL: https://willitrunai.com/can-run/codestral-2-25.08-on-a5000-24gb

⇱ Codestral 2 25.08 on RTX A5000 24GB? YES