Can Codestral 22B v0.1 run on RTX 4500 Ada 24GB?

YES — Runs Great

C53Usable

Estimated from fit model

Codestral 22B v0.1 needs ~19.6 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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, 25.4 tok/s, Runs well

19.6 GB required24.0 GB available

82% VRAM used

Fit status

Runs well

Decode

25.4 tok/s

TTFT

7613 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 4500 Ada 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: 25.4 tok/s decode · 7.6s TTFT (warm) · 64 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	25.4 tok/s	4152 ms	43K
Coding	C	Runs well	25.4 tok/s	7613 ms	43K
Agentic Coding	C	Tight fit	25.4 tok/s	11073 ms	43K
Reasoning	C	Runs well	25.4 tok/s	8997 ms	43K
RAG	C	Tight fit	25.4 tok/s	13842 ms	43K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on RTX 4500 Ada 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-lmstudio-community--codestral-22b-v0-1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Codestral 22B v0.1

RTX 5090 32GBOpción económica

32 GB VRAM (+8)1792 GB/s (+1360)

Sube la velocidad estimada de decodificación alrededor de un 252%.89.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 252%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMejor relación calidad-precio

32 GB VRAM (+8)896 GB/s (+464)

Sube la velocidad estimada de decodificación alrededor de un 121%.56.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 121%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

NVIDIA V100 32GBMejora NVIDIA

32 GB VRAM (+8)900 GB/s (+468)

Sube la velocidad estimada de decodificación alrededor de un 77%.44.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 77%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$8,999 MSRP

Frequently asked questions

Can RTX 4500 Ada 24GB run Codestral 22B v0.1?

Yes, RTX 4500 Ada 24GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 25.4 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 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Codestral 22B v0.1 achieves approximately 25.4 tokens per second decode speed with a time-to-first-token of 7613ms using Q4_K_M quantization.

Can RTX 4500 Ada 24GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on RTX 4500 Ada 24GB receives a C grade with 25.4 tok/s and 43K context.

What context window can Codestral 22B v0.1 use on RTX 4500 Ada 24GB?

On RTX 4500 Ada 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 4500 Ada 24GB See all hardware for Codestral 22B v0.1

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