Can Codestral 21B Pruned i1 run on NVIDIA A30 24GB?

YES — Runs Great

C55Usable

○Estimated from fit model

Codestral 21B Pruned i1 needs ~18.9 GB VRAM. NVIDIA A30 24GB has 24.0 GB. With Q4_K_M quantization, expect ~57 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, 56.8 tok/s, Runs well

18.9 GB required24.0 GB available

79% VRAM used

Fit status

Runs well

Decode

56.8 tok/s

TTFT

3408 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 NVIDIA A30 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: 56.8 tok/s decode · 3.4s TTFT (warm) · 142 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	56.8 tok/s	1859 ms	49K
Coding	C	Runs well	56.8 tok/s	3408 ms	49K
Agentic Coding	C	Tight fit	56.8 tok/s	4957 ms	49K
Reasoning	C	Runs well	56.8 tok/s	4027 ms	49K
RAG	C	Tight fit	56.8 tok/s	6196 ms	49K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on NVIDIA A30 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

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

See all results for NVIDIA A30 24GB See all hardware for Codestral 21B Pruned i1

URL: https://willitrunai.com/can-run/hf-mradermacher--codestral-21b-pruned-i1-gguf-on-a30-24gb

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