Can Codestral 22B run on RTX 4000 Ada 20GB?

YES — With Offload

B59Good

Estimated from fit model

Codestral 22B needs ~19.1 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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) — 19.1 GB, 22.5 tok/s, Runs with offload

19.1 GB required20.0 GB available

96% VRAM used

Fit status

Runs with offload

Decode

22.5 tok/s

TTFT

8607 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 4000 Ada 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: 22.5 tok/s decode · 8.6s TTFT (warm) · 56 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	22.5 tok/s	4695 ms	22K
Coding	B	Runs with offload	22.5 tok/s	8607 ms	22K
Agentic Coding	C	Runs with offload (needs ~0.9 GB host RAM)	14.5 tok/s	19443 ms	22K
Reasoning	B	Runs with offload	22.5 tok/s	10172 ms	22K
RAG	C	Runs with offload (needs ~0.9 GB host RAM)	14.5 tok/s	24303 ms	22K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on RTX 4000 Ada 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	12.3 GB	Medium	B60
Q4_K_MBest for your GPU	4	13.4 GB	Medium	B60
Q5_K_M	5	15.8 GB	High	F0
Q6_K	6	18.0 GB	High	F0
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 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 (+576)

Raises estimated decode speed by about 133%.52.5 tok/s decode

Raises estimated decode speed by about 133%.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+648)

Raises estimated decode speed by about 173%.61.4 tok/s decode

Raises estimated decode speed by about 173%.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+4)672 GB/s (+312)

Raises estimated decode speed by about 101%.45.2 tok/s decode

Raises estimated decode speed by about 101%.

~$1,599 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run Codestral 22B?

Yes, RTX 4000 Ada 20GB can run Codestral 22B with a B grade (Runs with offload). Expected decode speed: 22.5 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Codestral 22B achieves approximately 22.5 tokens per second decode speed with a time-to-first-token of 8607ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on RTX 4000 Ada 20GB receives a B grade with 22.5 tok/s and 22K context.

What context window can Codestral 22B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 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 4000 Ada 20GB?

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

See all results for RTX 4000 Ada 20GB See all hardware for Codestral 22B

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