Can Codestral 22B run on NVIDIA A40 48GB?

YES — Runs Great

B59Good

○Estimated from fit model

Codestral 22B needs ~21.9 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~44 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) — 21.9 GB, 43.5 tok/s, Runs well

21.9 GB required48.0 GB available

46% VRAM used

Fit status

Runs well

Decode

43.5 tok/s

TTFT

4452 ms

Safe context

33K

Memory

21.9 GB / 48.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodestral 22B on NVIDIA A40 48GB
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: 43.5 tok/s decode · 4.5s TTFT (warm) · 109 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	B	Runs well	43.5 tok/s	2428 ms	33K
Coding	B	Runs well	43.5 tok/s	4452 ms	33K
Agentic Coding	B	Runs well	43.5 tok/s	6475 ms	33K
Reasoning	B	Runs well	43.5 tok/s	5261 ms	33K
RAG	B	Runs well	43.5 tok/s	8094 ms	33K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C53
Q3_K_S	3	10.8 GB	Low	C53
NVFP4	4	12.3 GB	Medium	C54
Q4_K_M	4	13.4 GB	Medium	C54
Q5_K_M	5	15.8 GB	High	C55
Q6_K	6	18.0 GB	High	B55
Q8_0Best for your GPU	8	23.5 GB	Very High	B57
F16	16	45.1 GB	Maximum	F0

Get started

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

Run

ollama run codestral

Upgrade options

Hardware that runs Codestral 22B well

AMD Instinct MI210 64GBBudget pick

64 GB VRAM (+16)1638 GB/s (+942)

Raises estimated decode speed by about 105%.89.2 tok/s decode

Raises estimated decode speed by about 105%.

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

~$10,000 MSRP

Frequently asked questions

See all results for NVIDIA A40 48GB See all hardware for Codestral 22B

URL: https://willitrunai.com/can-run/codestral-22b-on-a40-48gb