Can Codestral 21B Pruned i1 run on RTX 5090 Laptop 24GB?

YES — Runs Great

C55Usable

Estimated from fit model

Codestral 21B Pruned i1 needs ~18.9 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 18.9 GB, 58.8 tok/s, Runs well

18.9 GB required24.0 GB available

79% VRAM used

Fit status

Runs well

Decode

58.8 tok/s

TTFT

3295 ms

Safe context

49K

Memory

18.9 GB / 24.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 on RTX 5090 Laptop 24GB
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: 58.8 tok/s decode · 3.3s TTFT (warm) · 147 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	58.8 tok/s	1797 ms	49K
Coding	C	Runs well	58.8 tok/s	3295 ms	49K
Agentic Coding	C	Tight fit	58.8 tok/s	4793 ms	49K
Reasoning	C	Runs well	58.8 tok/s	3894 ms	49K
RAG	C	Tight fit	58.8 tok/s	5991 ms	49K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	C47
Q3_K_S	3	10.3 GB	Low	C49
NVFP4	4	11.8 GB	Medium	C50
Q4_K_M	4	12.8 GB	Medium	C50
Q5_K_M	5	15.1 GB	High	C49
Q6_KBest for your GPU	6	17.2 GB	High	C49
Q8_0	8	22.5 GB	Very High	F0
F16	16	43.1 GB	Maximum	F0

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

Frequently asked questions

Can RTX 5090 Laptop 24GB run Codestral 21B Pruned i1?

Yes, RTX 5090 Laptop 24GB can run Codestral 21B Pruned i1 with a C grade (Runs well). Expected decode speed: 58.8 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 18.9 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 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, Codestral 21B Pruned i1 achieves approximately 58.8 tokens per second decode speed with a time-to-first-token of 3295ms using Q4_K_M quantization.

Can RTX 5090 Laptop 24GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on RTX 5090 Laptop 24GB receives a C grade with 58.8 tok/s and 49K context.

What context window can Codestral 21B Pruned i1 use on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, Codestral 21B Pruned i1 can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5090 Laptop 24GB See all hardware for Codestral 21B Pruned i1

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