Can Codestral 21B Pruned i1 run on RTX 5000 Ada 32GB?

YES — Runs Great

C52Usable

Estimated from fit model

Codestral 21B Pruned i1 needs ~19.7 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~36 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) — 19.7 GB, 36.0 tok/s, Runs well

19.7 GB required32.0 GB available

62% VRAM used

Fit status

Runs well

Decode

36.0 tok/s

TTFT

5382 ms

Safe context

96K

Memory

19.7 GB / 32.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 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: 36.0 tok/s decode · 5.4s TTFT (warm) · 90 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	36.0 tok/s	2936 ms	96K
Coding	C	Runs well	36.0 tok/s	5382 ms	96K
Agentic Coding	C	Runs well	36.0 tok/s	7828 ms	96K
Reasoning	C	Runs well	36.0 tok/s	6361 ms	96K
RAG	C	Runs well	36.0 tok/s	9785 ms	96K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	C45
Q3_K_S	3	10.3 GB	Low	C46
NVFP4	4

Get started

Copy-paste commands to run Codestral 21B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-21b-pruned-i1-gguf && lms server start

Upgrade options

Hardware that runs Codestral 21B Pruned i1 well

MacBook Pro M4 Max 48GBBest value

48 GB Unified (+16)

This setup is broadly balanced for this model.35.2 tok/s decode

~$2,499 MSRP

NVIDIA A100 40GBBudget pick

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

Raises estimated decode speed by about 183%.102 tok/s decode

Raises estimated decode speed by about 183%.

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

~$10,000 MSRP

Frequently asked questions

Can RTX 5000 Ada 32GB run Codestral 21B Pruned i1?

Yes, RTX 5000 Ada 32GB can run Codestral 21B Pruned i1 with a C grade (Runs well). Expected decode speed: 36.0 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 19.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 21B Pruned i1?

The recommended quantization for Codestral 21B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 21B Pruned i1 run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Codestral 21B Pruned i1 achieves approximately 36.0 tokens per second decode speed with a time-to-first-token of 5382ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on RTX 5000 Ada 32GB receives a C grade with 36.0 tok/s and 96K context.

What context window can Codestral 21B Pruned i1 use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Codestral 21B Pruned i1 can safely use up to 96K 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 21B Pruned i1

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