Can Codestral 22B v0.1 run on NVIDIA A100 40GB?

YES — Runs Great

C53Usable

○Estimated from fit model

Codestral 22B v0.1 needs ~21.2 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~97 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) — 21.2 GB, 97.3 tok/s, Runs well

21.2 GB required40.0 GB available

53% VRAM used

Fit status

Runs well

Decode

97.3 tok/s

TTFT

1989 ms

Safe context

133K

Memory

21.2 GB / 40.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 on NVIDIA A100 40GB
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: 97.3 tok/s decode · 2.0s TTFT (warm) · 243 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	97.3 tok/s	1085 ms	133K
Coding	C	Runs well	97.3 tok/s	1989 ms	133K
Agentic Coding	C	Runs well	97.3 tok/s	2893 ms	133K
Reasoning	C	Runs well	97.3 tok/s	2351 ms	133K
RAG	C	Runs well	97.3 tok/s	3616 ms	133K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C44
Q3_K_S	3	10.8 GB	Low	C44
NVFP4	4	12.3 GB	Medium	C45
Q4_K_M	4	13.4 GB	Medium	C45
Q5_K_M	5	15.8 GB	High	C46
Q6_K	6	18.0 GB	High	C47
Q8_0Best for your GPU	8	23.5 GB	Very High	C49
F16	16	45.1 GB	Maximum	F0

Get started

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

Run

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

Frequently asked questions

See all results for NVIDIA A100 40GB See all hardware for Codestral 22B v0.1

URL: https://willitrunai.com/can-run/hf-bartowski--codestral-22b-v0-1-gguf-on-a100-40gb

⇱ Codestral 22B v0.1 on NVIDIA A100 40GB? YES