Can Codestral 22B v0.1 run on Tesla P40 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

Codestral 22B v0.1 needs ~19.6 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~15 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) — 19.6 GB, 15.2 tok/s, Runs well

19.6 GB required24.0 GB available

82% VRAM used

Fit status

Runs well

Decode

15.2 tok/s

TTFT

12727 ms

Safe context

43K

Memory

19.6 GB / 24.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 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: 15.2 tok/s decode · 12.7s TTFT (warm) · 38 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	15.2 tok/s	6942 ms	43K
Coding	C	Runs well	15.2 tok/s	12727 ms	43K
Agentic Coding	C	Tight fit	15.2 tok/s	18512 ms	43K
Reasoning	C	Runs well	15.2 tok/s	15041 ms	43K
RAG	C	Tight fit	15.2 tok/s	23140 ms	43K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C48
Q3_K_S	3	10.8 GB	Low	C49
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

RTX 5090 32GBBudget pick

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

Raises estimated decode speed by about 489%.89.5 tok/s decode

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 32GBBest value

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

Raises estimated decode speed by about 269%.56.1 tok/s decode

Raises estimated decode speed by about 269%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

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

Raises estimated decode speed by about 126%.34.3 tok/s decode

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 22B v0.1?

Yes, Tesla P40 24GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 15.2 tok/s.

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

Codestral 22B v0.1 (22B parameters) requires approximately 19.6 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 Tesla P40 24GB?

On Tesla P40 24GB, Codestral 22B v0.1 achieves approximately 15.2 tokens per second decode speed with a time-to-first-token of 12727ms using Q4_K_M quantization.

Can Tesla P40 24GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on Tesla P40 24GB receives a C grade with 15.2 tok/s and 43K context.

What context window can Codestral 22B v0.1 use on Tesla P40 24GB?

On Tesla P40 24GB, Codestral 22B v0.1 can safely use up to 43K 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 22B v0.1

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