Can Codestral 22B run on RTX A4500 20GB?

Q: What should I upgrade first if Codestral 22B feels slow on RTX A4500 20GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

B61Good

Estimated from fit model

Codestral 22B needs ~19.1 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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) — 19.1 GB, 40.0 tok/s, Runs with offload

19.1 GB required20.0 GB available

96% VRAM used

Fit status

Runs with offload

Decode

40.0 tok/s

TTFT

4841 ms

Safe context

22K

Memory

19.1 GB / 20.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsCodestral 22B on RTX A4500 20GB
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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Tight fit	37.2 tok/s	2839 ms	22K
Coding	B	Runs with offload	37.2 tok/s	5205 ms	22K
Agentic Coding	C	Runs with offload	24.0 tok/s	11757 ms	22K
Reasoning	B	Runs with offload	37.2 tok/s	6151 ms	22K
RAG	C	Runs with offload	24.0 tok/s	14696 ms	22K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	B60
Q3_K_S	3	10.8 GB	Low	B61
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 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+296)

Raises estimated decode speed by about 31%.52.5 tok/s decode

Raises estimated decode speed by about 31%.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+368)

Raises estimated decode speed by about 53%.61.4 tok/s decode

Raises estimated decode speed by about 53%.

~$1,599 MSRP

RTX A5500 24GBNVIDIA upgrade

24 GB VRAM (+4)768 GB/s (+128)

This setup is broadly balanced for this model.48 tok/s decode

~$3,200 MSRP

Frequently asked questions

Can RTX A4500 20GB run Codestral 22B?

Yes, RTX A4500 20GB can run Codestral 22B with a B grade (Runs with offload). Expected decode speed: 37.2 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 19.1 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 A4500 20GB?

On RTX A4500 20GB, Codestral 22B achieves approximately 37.2 tokens per second decode speed with a time-to-first-token of 5205ms using Q4_K_M quantization.

Can RTX A4500 20GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on RTX A4500 20GB receives a B grade with 37.2 tok/s and 22K context.

What context window can Codestral 22B use on RTX A4500 20GB?

On RTX A4500 20GB, Codestral 22B can safely use up to 22K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 22B feels slow on RTX A4500 20GB?

See all results for RTX A4500 20GB See all hardware for Codestral 22B

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