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

YES — Runs Great

B63Good

Estimated from fit model

Codestral 22B needs ~19.5 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~27 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) — 19.5 GB, 27.3 tok/s, Runs well

19.5 GB required24.0 GB available

81% VRAM used

Fit status

Runs well

Decode

27.3 tok/s

TTFT

7082 ms

Safe context

33K

Memory

19.5 GB / 24.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral 22B 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: 27.3 tok/s decode · 7.1s TTFT (warm) · 68 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	B	Runs well	27.3 tok/s	3863 ms	33K
Coding	B	Runs well	27.3 tok/s	7082 ms	33K
Agentic Coding	B	Tight fit	27.3 tok/s	10301 ms	33K
Reasoning	B	Runs well	27.3 tok/s	8369 ms	33K
RAG	B	Tight fit	27.3 tok/s	12876 ms	33K

Quantization options

How Codestral 22B (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	B58
Q3_K_S	3	10.8 GB	Low	B60
NVFP4	4

Get started

Copy-paste commands to run Codestral 22B on your machine.

Run

ollama run codestral

Upgrade options

Hardware that runs Codestral 22B well

RTX 5090 32GBBudget pick

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

Raises estimated decode speed by about 252%.96.2 tok/s decode

Raises estimated decode speed by about 252%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

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

Raises estimated decode speed by about 121%.60.3 tok/s decode

Raises estimated decode speed by about 121%.

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

~$2,499 MSRP

NVIDIA V100 32GBNVIDIA upgrade

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

Raises estimated decode speed by about 77%.48.3 tok/s decode

Raises estimated decode speed by about 77%.

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

~$8,999 MSRP

Frequently asked questions

Can RTX 4500 Ada 24GB run Codestral 22B?

Yes, RTX 4500 Ada 24GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 27.3 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 19.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B?

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

What speed will Codestral 22B run at on RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Codestral 22B achieves approximately 27.3 tokens per second decode speed with a time-to-first-token of 7082ms using Q4_K_M quantization.

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

For coding workloads, Codestral 22B on RTX 4500 Ada 24GB receives a B grade with 27.3 tok/s and 33K context.

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

On RTX 4500 Ada 24GB, Codestral 22B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for RTX 4500 Ada 24GB See all hardware for Codestral 22B

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