Can Codestral 22B run on Quadro RTX 8000 48GB?

YES — Runs Great

B59Good

Estimated from fit model

Codestral 22B needs ~21.9 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 21.9 GB, 37.1 tok/s, Runs well

21.9 GB required48.0 GB available

46% VRAM used

Fit status

Runs well

Decode

37.1 tok/s

TTFT

5212 ms

Safe context

33K

Memory

21.9 GB / 48.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodestral 22B on Quadro RTX 8000 48GB
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: 37.1 tok/s decode · 5.2s TTFT (warm) · 93 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	B	Runs well	37.1 tok/s	2843 ms	33K
Coding	B	Runs well	37.1 tok/s	5212 ms	33K
Agentic Coding	B	Runs well	37.1 tok/s	7582 ms	33K
Reasoning	B	Runs well	37.1 tok/s	6160 ms	33K
RAG	B	Runs well	37.1 tok/s	9477 ms	33K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C53
Q3_K_S	3	10.8 GB	Low	C53
NVFP4	4	12.3 GB	Medium	C54
Q4_K_M	4	13.4 GB	Medium	C54
Q5_K_M	5	15.8 GB	High	C55
Q6_K	6	18.0 GB	High	B55
Q8_0Best for your GPU	8	23.5 GB	Very High	B57
F16	16	45.1 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral 22B on your machine.

Run

ollama run codestral

Opciones de mejora

Hardware que ejecuta bien Codestral 22B

AMD Instinct MI210 64GBOpción económica

64 GB VRAM (+16)1638 GB/s (+966)

Sube la velocidad estimada de decodificación alrededor de un 140%.89.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 140%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$10,000 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run Codestral 22B?

Yes, Quadro RTX 8000 48GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 37.1 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 21.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B?

The recommended quantization for Codestral 22B is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B run at on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Codestral 22B achieves approximately 37.1 tokens per second decode speed with a time-to-first-token of 5212ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on Quadro RTX 8000 48GB receives a B grade with 37.1 tok/s and 33K context.

What context window can Codestral 22B use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Codestral 22B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GB See all hardware for Codestral 22B

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