Can Codestral 22B v0.1 IMat run on RTX 5000 Ada 32GB?

YES — Runs Great

C52Usable

Estimated from fit model

Codestral 22B v0.1 IMat needs ~20.4 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 20.4 GB, 34.3 tok/s, Runs well

20.4 GB required32.0 GB available

64% VRAM used

Fit status

Runs well

Decode

34.3 tok/s

TTFT

5638 ms

Safe context

88K

Memory

20.4 GB / 32.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 IMat on RTX 5000 Ada 32GB
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: 34.3 tok/s decode · 5.6s TTFT (warm) · 86 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	34.3 tok/s	3075 ms	88K
Coding	C	Runs well	34.3 tok/s	5638 ms	88K
Agentic Coding	C	Runs well	34.3 tok/s	8201 ms	88K
Reasoning	C	Runs well	34.3 tok/s	6663 ms	88K
RAG	C	Runs well	34.3 tok/s	10251 ms	88K

Quantization options

How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C45
Q3_K_S	3	10.8 GB	Low	C46
NVFP4	4	12.3 GB	Medium	C47
Q4_K_M	4	13.4 GB	Medium	C47
Q5_K_M	5	15.8 GB	High	C49
Q6_K	6	18.0 GB	High	C49
Q8_0Best for your GPU	8	23.5 GB	Very High	C48
F16	16	45.1 GB	Maximum	F0

Get started

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

Run

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

Upgrade options

Hardware that runs Codestral 22B v0.1 IMat well

MacBook Pro M4 Max 48GBBest value

48 GB Unified (+16)

This setup is broadly balanced for this model.34.8 tok/s decode

~$2,499 MSRP

NVIDIA A100 40GBBudget pick

40 GB VRAM (+8)1555 GB/s (+979)

Raises estimated decode speed by about 184%.97.3 tok/s decode

Raises estimated decode speed by about 184%.

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

~$10,000 MSRP

Frequently asked questions

Can RTX 5000 Ada 32GB run Codestral 22B v0.1 IMat?

Yes, RTX 5000 Ada 32GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 34.3 tok/s.

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

Codestral 22B v0.1 IMat (22B parameters) requires approximately 20.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1 IMat?

The recommended quantization for Codestral 22B v0.1 IMat is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 IMat run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Codestral 22B v0.1 IMat achieves approximately 34.3 tokens per second decode speed with a time-to-first-token of 5638ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on RTX 5000 Ada 32GB receives a C grade with 34.3 tok/s and 88K context.

What context window can Codestral 22B v0.1 IMat use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Codestral 22B v0.1 IMat can safely use up to 88K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for Codestral 22B v0.1 IMat

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<iframe src="https://willitrunai.com/embed/hf-legraphista--codestral-22b-v0-1-imat-gguf-on-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

34.3 tok/s

3075 ms

88K

Coding

Runs well

34.3 tok/s

5638 ms

88K

Agentic Coding

Runs well

34.3 tok/s

8201 ms

88K

Reasoning

Runs well

34.3 tok/s

6663 ms

88K

RAG

Runs well

34.3 tok/s

10251 ms

88K

Quantization options

How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C45
Q3_K_S	3	10.8 GB	Low	C46
NVFP4	4	12.3 GB	Medium	C47
Q4_K_M	4	13.4 GB	Medium	C47
Q5_K_M	5	15.8 GB	High	C49
Q6_K	6	18.0 GB	High	C49
Q8_0Best for your GPU	8	23.5 GB	Very High	C48
F16	16	45.1 GB	Maximum	F0

Frequently asked questions

Can RTX 5000 Ada 32GB run Codestral 22B v0.1 IMat?

Yes, RTX 5000 Ada 32GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 34.3 tok/s.

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

Codestral 22B v0.1 IMat (22B parameters) requires approximately 20.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1 IMat?

The recommended quantization for Codestral 22B v0.1 IMat is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 IMat run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Codestral 22B v0.1 IMat achieves approximately 34.3 tokens per second decode speed with a time-to-first-token of 5638ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on RTX 5000 Ada 32GB receives a C grade with 34.3 tok/s and 88K context.

What context window can Codestral 22B v0.1 IMat use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Codestral 22B v0.1 IMat can safely use up to 88K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.