Can Codestral 22B v0.1 run on RTX 4090 24GB?

YES — Runs Great

B55Good

Estimated from fit model

Codestral 22B v0.1 needs ~19.6 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~57 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) — 19.6 GB, 57.1 tok/s, Runs well

19.6 GB required24.0 GB available

82% VRAM used

Fit status

Runs well

Decode

57.1 tok/s

TTFT

3391 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 RTX 4090 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: 57.1 tok/s decode · 3.4s TTFT (warm) · 143 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	B	Runs well	57.1 tok/s	1850 ms	43K
Coding	B	Runs well	57.1 tok/s	3391 ms	43K
Agentic Coding	C	Tight fit	57.1 tok/s	4933 ms	43K
Reasoning	B	Runs well	57.1 tok/s	4008 ms	43K
RAG	C	Tight fit	57.1 tok/s	6166 ms	43K

Inference speed

Codestral 22B v0.1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral 22B v0.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~90 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	89.5	Fits
RTX 4090 24GB	24 GB	Q4_K_M	57.1	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	51.5	Fits
RTX 3090 24GB	24 GB	Q4_K_M	48.8	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	41.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	34.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	34.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.6	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	24.3	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.9	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	8.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on RTX 4090 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	12.3 GB	Medium	C50
Q4_K_M	4	13.4 GB	Medium	C50
Q5_K_M	5	15.8 GB	High	C50
Q6_KBest for your GPU	6	18.0 GB	High	C49
Q8_0	8	23.5 GB	Very High	F0
F16	16	45.1 GB	Maximum	F0

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

Opciones de mejora

Hardware que ejecuta bien Codestral 22B v0.1

RTX 5090 32GBOpción económica

32 GB VRAM (+8)1792 GB/s (+784)

Sube la velocidad estimada de decodificación alrededor de un 57%.89.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 57%.

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

~$1,999 MSRP

Frequently asked questions

Can RTX 4090 24GB run Codestral 22B v0.1?

Yes, RTX 4090 24GB can run Codestral 22B v0.1 with a B grade (Runs well). Expected decode speed: 57.1 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 RTX 4090 24GB?

On RTX 4090 24GB, Codestral 22B v0.1 achieves approximately 57.1 tokens per second decode speed with a time-to-first-token of 3391ms using Q4_K_M quantization.

Can RTX 4090 24GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on RTX 4090 24GB receives a B grade with 57.1 tok/s and 43K context.

What context window can Codestral 22B v0.1 use on RTX 4090 24GB?

On RTX 4090 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 RTX 4090 24GB See all hardware for Codestral 22B v0.1

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