Can Codestral 22B v0.1 run on NVIDIA A100 80GB?

YES — Runs Great

C49Usable

Estimated from fit model

Codestral 22B v0.1 needs ~25.2 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~128 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) — 25.2 GB, 127.6 tok/s, Runs well

25.2 GB required80.0 GB available

32% VRAM used

Fit status

Runs well

Decode

127.6 tok/s

TTFT

1517 ms

Safe context

356K

Memory

25.2 GB / 80.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 on NVIDIA A100 80GB
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: 127.6 tok/s decode · 1.5s TTFT (warm) · 319 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	127.6 tok/s	827 ms	356K
Coding	C	Runs well	127.6 tok/s	1517 ms	356K
Agentic Coding	C	Runs well	127.6 tok/s	2206 ms	356K
Reasoning	C	Runs well	127.6 tok/s	1793 ms	356K
RAG	C	Runs well	127.6 tok/s	2758 ms	356K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C40
Q3_K_S	3	10.8 GB	Low	C40
NVFP4	4	12.3 GB	Medium	C40
Q4_K_M	4	13.4 GB	Medium	C41
Q5_K_M	5	15.8 GB	High	C41
Q6_K	6	18.0 GB	High	C41
Q8_0	8	23.5 GB	Very High	C42
F16Best for your GPU	16	45.1 GB	Maximum	C48

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

Can NVIDIA A100 80GB run Codestral 22B v0.1?

Yes, NVIDIA A100 80GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 127.6 tok/s.

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

Codestral 22B v0.1 (22B parameters) requires approximately 25.2 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 A100 80GB?

On NVIDIA A100 80GB, Codestral 22B v0.1 achieves approximately 127.6 tokens per second decode speed with a time-to-first-token of 1517ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on NVIDIA A100 80GB receives a C grade with 127.6 tok/s and 356K context.

What context window can Codestral 22B v0.1 use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Codestral 22B v0.1 can safely use up to 356K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Codestral 22B v0.1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-bartowski--codestral-22b-v0-1-gguf-on-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

127.6 tok/s

827 ms

356K

Coding

Runs well

127.6 tok/s

1517 ms

356K

Agentic Coding

Runs well

127.6 tok/s

2206 ms

356K

Reasoning

Runs well

127.6 tok/s

1793 ms

356K

RAG

Runs well

127.6 tok/s

2758 ms

356K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C40
Q3_K_S	3	10.8 GB	Low	C40
NVFP4	4	12.3 GB	Medium	C40
Q4_K_M	4	13.4 GB	Medium	C41
Q5_K_M	5	15.8 GB	High	C41
Q6_K	6	18.0 GB	High	C41
Q8_0	8	23.5 GB	Very High	C42
F16Best for your GPU	16	45.1 GB	Maximum	C48

Frequently asked questions

Can NVIDIA A100 80GB run Codestral 22B v0.1?

Yes, NVIDIA A100 80GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 127.6 tok/s.

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

Codestral 22B v0.1 (22B parameters) requires approximately 25.2 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 A100 80GB?

On NVIDIA A100 80GB, Codestral 22B v0.1 achieves approximately 127.6 tokens per second decode speed with a time-to-first-token of 1517ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on NVIDIA A100 80GB receives a C grade with 127.6 tok/s and 356K context.

What context window can Codestral 22B v0.1 use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Codestral 22B v0.1 can safely use up to 356K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.