Can Codestral 22B v0.1 run on RTX 4000 Ada 20GB?

Q: What should I upgrade first if Codestral 22B v0.1 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.

YES — With Offload

C49Usable

Estimated from fit model

Codestral 22B v0.1 needs ~19.2 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~21 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) — 19.2 GB, 20.9 tok/s, Runs with offload

19.2 GB required20.0 GB available

96% VRAM used

Fit status

Runs with offload

Decode

20.9 tok/s

TTFT

9253 ms

Safe context

21K

Memory

19.2 GB / 20.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 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: 20.9 tok/s decode · 9.3s TTFT (warm) · 52 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	C	Tight fit	20.9 tok/s	5047 ms	21K
Coding	C	Runs with offload	20.9 tok/s	9253 ms	21K
Agentic Coding	D	Very compromised (needs ~1.1 GB host RAM)	13.1 tok/s	21464 ms	21K
Reasoning	C	Runs with offload	20.9 tok/s	10935 ms	21K
RAG	D	Very compromised (needs ~1.1 GB host RAM)	13.1 tok/s	26830 ms

Quantization options

How Codestral 22B v0.1 (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	C50
Q3_K_S	3	10.8 GB	Low	C50
NVFP4	4

Get started

Copy-paste commands to run Codestral 22B v0.1 on your machine.

Run

lms load hf-sanctumai--codestral-22b-v0-1-gguf && lms server start

Upgrade options

Hardware that runs Codestral 22B v0.1 well

RTX 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+576)

Raises estimated decode speed by about 133%.48.8 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%.57.1 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%.42.1 tok/s decode

Raises estimated decode speed by about 101%.

~$1,599 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run Codestral 22B v0.1?

Yes, RTX 4000 Ada 20GB can run Codestral 22B v0.1 with a C grade (Runs with offload). Expected decode speed: 20.9 tok/s.

How much VRAM does Codestral 22B v0.1 need?

Codestral 22B v0.1 (22B parameters) requires approximately 19.2 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Codestral 22B v0.1 achieves approximately 20.9 tokens per second decode speed with a time-to-first-token of 9253ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on RTX 4000 Ada 20GB receives a C grade with 20.9 tok/s and 21K context.

What context window can Codestral 22B v0.1 use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Codestral 22B v0.1 can safely use up to 21K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 22B v0.1 feels slow on RTX 4000 Ada 20GB?

See all results for RTX 4000 Ada 20GB See all hardware for Codestral 22B v0.1

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