Can Codestral 21B Pruned i1 run on Tesla P40 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

Codestral 21B Pruned i1 needs ~18.9 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 18.9 GB, 15.9 tok/s, Runs well

18.9 GB required24.0 GB available

79% VRAM used

Fit status

Runs well

Decode

15.9 tok/s

TTFT

12149 ms

Safe context

49K

Memory

18.9 GB / 24.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 on Tesla P40 24GB
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: 15.9 tok/s decode · 12.1s TTFT (warm) · 40 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	15.9 tok/s	6627 ms	49K
Coding	C	Runs well	15.9 tok/s	12149 ms	49K
Agentic Coding	C	Tight fit	15.9 tok/s	17671 ms	49K
Reasoning	C	Runs well	15.9 tok/s	14357 ms	49K
RAG	C	Tight fit	15.9 tok/s	22088 ms	49K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	C47
Q3_K_S	3	10.3 GB	Low	C49
NVFP4	4	11.8 GB	Medium	C50
Q4_K_M	4	12.8 GB	Medium	C50
Q5_K_M	5	15.1 GB	High	C49
Q6_KBest for your GPU	6	17.2 GB	High	C49
Q8_0	8	22.5 GB	Very High	F0
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

Opções de upgrade

Hardware que roda bem Codestral 21B Pruned i1

RTX 5090 32GBOpção econômica

32 GB VRAM (+8)1792 GB/s (+1446)

Raises estimated decode speed by about 489%.93.7 tok/s decodificação

Raises estimated decode speed by about 489%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMelhor custo-benefício

32 GB VRAM (+8)896 GB/s (+550)

Raises estimated decode speed by about 270%.58.8 tok/s decodificação

Raises estimated decode speed by about 270%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBUpgrade NVIDIA

32 GB VRAM (+8)576 GB/s (+230)

Raises estimated decode speed by about 126%.36 tok/s decodificação

Raises estimated decode speed by about 126%.

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

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run Codestral 21B Pruned i1?

Yes, Tesla P40 24GB can run Codestral 21B Pruned i1 with a C grade (Runs well). Expected decode speed: 15.9 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 18.9 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 Tesla P40 24GB?

On Tesla P40 24GB, Codestral 21B Pruned i1 achieves approximately 15.9 tokens per second decode speed with a time-to-first-token of 12149ms using Q4_K_M quantization.

Can Tesla P40 24GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on Tesla P40 24GB receives a C grade with 15.9 tok/s and 49K context.

What context window can Codestral 21B Pruned i1 use on Tesla P40 24GB?

On Tesla P40 24GB, Codestral 21B Pruned i1 can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Codestral 21B Pruned i1

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