Can Codestral 21B Pruned i1 run on NVIDIA V100 32GB?

YES — Runs Great

C52Usable

Estimated from fit model

Codestral 21B Pruned i1 needs ~19.7 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~47 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) — 19.7 GB, 47.1 tok/s, Runs well

19.7 GB required32.0 GB available

62% VRAM used

Fit status

Runs well

Decode

47.1 tok/s

TTFT

4113 ms

Safe context

96K

Memory

19.7 GB / 32.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 on NVIDIA V100 32GB
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: 47.1 tok/s decode · 4.1s TTFT (warm) · 118 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	47.1 tok/s	2243 ms	96K
Coding	C	Runs well	47.1 tok/s	4113 ms	96K
Agentic Coding	C	Runs well	47.1 tok/s	5982 ms	96K
Reasoning	C	Runs well	47.1 tok/s	4861 ms	96K
RAG	C	Runs well	47.1 tok/s	7478 ms	96K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	C45
Q3_K_S	3	10.3 GB	Low	C46
NVFP4	4	11.8 GB	Medium	C46
Q4_K_M	4	12.8 GB	Medium	C47
Q5_K_M	5	15.1 GB	High	C48
Q6_K	6	17.2 GB	High	C49
Q8_0Best for your GPU	8	22.5 GB	Very High	C48
F16	16	43.1 GB	Maximum	F0

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 V100 32GB run Codestral 21B Pruned i1?

Yes, NVIDIA V100 32GB can run Codestral 21B Pruned i1 with a C grade (Runs well). Expected decode speed: 47.1 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 19.7 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 V100 32GB?

On NVIDIA V100 32GB, Codestral 21B Pruned i1 achieves approximately 47.1 tokens per second decode speed with a time-to-first-token of 4113ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on NVIDIA V100 32GB receives a C grade with 47.1 tok/s and 96K context.

What context window can Codestral 21B Pruned i1 use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Codestral 21B Pruned i1 can safely use up to 96K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Codestral 21B Pruned i1

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