Can Codestral 22B v0.1 run on NVIDIA A40 48GB?

YES — Runs Great

C49Usable

Estimated from fit model

Codestral 22B v0.1 needs ~22.0 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~41 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) — 22.0 GB, 40.5 tok/s, Runs well

22.0 GB required48.0 GB available

46% VRAM used

Fit status

Runs well

Decode

40.5 tok/s

TTFT

4786 ms

Safe context

177K

Memory

22.0 GB / 48.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 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: 40.5 tok/s decode · 4.8s TTFT (warm) · 101 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	40.5 tok/s	2610 ms	177K
Coding	C	Runs well	40.5 tok/s	4786 ms	177K
Agentic Coding	C	Runs well	40.5 tok/s	6961 ms	177K
Reasoning	C	Runs well	40.5 tok/s	5656 ms	177K
RAG	C	Runs well	40.5 tok/s	8701 ms	177K

Quantization options

How Codestral 22B v0.1 (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	C42
Q3_K_S	3	10.8 GB	Low	C43
NVFP4	4

Get started

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

Run

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

Upgrade options

Hardware that runs Codestral 22B v0.1 well

AMD Instinct MI210 64GBBudget pick

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

Raises estimated decode speed by about 105%.83 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

Can NVIDIA A40 48GB run Codestral 22B v0.1?

Yes, NVIDIA A40 48GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 40.5 tok/s.

How much VRAM does Codestral 22B v0.1 need?

Codestral 22B v0.1 (22B parameters) requires approximately 22.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1?

The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 run at on NVIDIA A40 48GB?

On NVIDIA A40 48GB, Codestral 22B v0.1 achieves approximately 40.5 tokens per second decode speed with a time-to-first-token of 4786ms using Q4_K_M quantization.

Can NVIDIA A40 48GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on NVIDIA A40 48GB receives a C grade with 40.5 tok/s and 177K context.

What context window can Codestral 22B v0.1 use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, Codestral 22B v0.1 can safely use up to 177K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A40 48GB See all hardware for Codestral 22B v0.1

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