Can Codestral 21B Pruned i1 run on NVIDIA A100 80GB?

YES — Runs Great

C48Usable

Estimated from fit model

Codestral 21B Pruned i1 needs ~24.5 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~134 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) — 24.5 GB, 133.7 tok/s, Runs well

24.5 GB required80.0 GB available

31% VRAM used

Fit status

Runs well

Decode

133.7 tok/s

TTFT

1448 ms

Safe context

377K

Memory

24.5 GB / 80.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 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: 133.7 tok/s decode · 1.4s TTFT (warm) · 334 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	133.7 tok/s	790 ms	377K
Coding	C	Runs well	133.7 tok/s	1448 ms	377K
Agentic Coding	C	Runs well	133.7 tok/s	2106 ms	377K
Reasoning	C	Runs well	133.7 tok/s	1711 ms	377K
RAG	C	Runs well	133.7 tok/s	2633 ms	377K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	D40
Q3_K_S	3	10.3 GB	Low	D40
NVFP4	4	11.8 GB	Medium	D40
Q4_K_M	4	12.8 GB	Medium	C40
Q5_K_M	5	15.1 GB	High	C40
Q6_K	6	17.2 GB	High	C41
Q8_0	8	22.5 GB	Very High	C42
F16Best for your GPU	16	43.1 GB	Maximum	C47

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

Can NVIDIA A100 80GB run Codestral 21B Pruned i1?

Yes, NVIDIA A100 80GB can run Codestral 21B Pruned i1 with a C grade (Runs well). Expected decode speed: 133.7 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 24.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 21B Pruned i1?

The recommended quantization for Codestral 21B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 21B Pruned i1 run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Codestral 21B Pruned i1 achieves approximately 133.7 tokens per second decode speed with a time-to-first-token of 1448ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on NVIDIA A100 80GB receives a C grade with 133.7 tok/s and 377K context.

What context window can Codestral 21B Pruned i1 use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Codestral 21B Pruned i1 can safely use up to 377K 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 21B Pruned i1

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