Can Codestral 22B v0.1 run on Quadro RTX 8000 48GB?

YES — Runs Great

C48Usable

Estimated from fit model

Codestral 22B v0.1 needs ~22.0 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~35 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) — 22.0 GB, 34.6 tok/s, Runs well

22.0 GB required48.0 GB available

46% VRAM used

Fit status

Runs well

Decode

34.6 tok/s

TTFT

5603 ms

Safe context

177K

Memory

22.0 GB / 48.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 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: 34.6 tok/s decode · 5.6s TTFT (warm) · 86 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	34.6 tok/s	3056 ms	177K
Coding	C	Runs well	34.6 tok/s	5603 ms	177K
Agentic Coding	C	Runs well	34.6 tok/s	8150 ms	177K
Reasoning	C	Runs well	34.6 tok/s	6622 ms	177K
RAG	C	Runs well	34.6 tok/s	10188 ms	177K

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 Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C42
Q3_K_S	3	10.8 GB	Low	C43
NVFP4	4	12.3 GB	Medium	C44
Q4_K_M	4	13.4 GB	Medium	C44
Q5_K_M	5	15.8 GB	High	C45
Q6_K	6	18.0 GB	High	C45
Q8_0Best for your GPU	8	23.5 GB	Very High	C47
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

Upgrade options

Hardware that runs Codestral 22B v0.1 well

NVIDIA A100 80GBBudget pick

80 GB VRAM (+32)2039 GB/s (+1367)

Raises estimated decode speed by about 269%.127.6 tok/s decode

Raises estimated decode speed by about 269%.

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

~$15,000 MSRP

NVIDIA A800 80GBBest value

80 GB VRAM (+32)1935 GB/s (+1263)

Raises estimated decode speed by about 225%.112.5 tok/s decode

Raises estimated decode speed by about 225%.

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

~$15,000 MSRP

NVIDIA H800 80GBNVIDIA upgrade

80 GB VRAM (+32)3000 GB/s (+2328)

Raises estimated decode speed by about 423%.181.1 tok/s decode

Raises estimated decode speed by about 423%.

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

~$30,000 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run Codestral 22B v0.1?

Yes, Quadro RTX 8000 48GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 34.6 tok/s.

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

Codestral 22B v0.1 (22B parameters) requires approximately 22.0 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Codestral 22B v0.1 achieves approximately 34.6 tokens per second decode speed with a time-to-first-token of 5603ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on Quadro RTX 8000 48GB receives a C grade with 34.6 tok/s and 177K context.

What context window can Codestral 22B v0.1 use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Codestral 22B v0.1 can safely use up to 177K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GB See all hardware for Codestral 22B v0.1

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<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--codestral-22b-v0-1-gguf-on-quadro-rtx-8000-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

34.6 tok/s

3056 ms

177K

Coding

Runs well

34.6 tok/s

5603 ms

177K

Agentic Coding

Runs well

34.6 tok/s

8150 ms

177K

Reasoning

Runs well

34.6 tok/s

6622 ms

177K

RAG

Runs well

34.6 tok/s

10188 ms

177K

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

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

Quantization options

How Codestral 22B v0.1 (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	C42
Q3_K_S	3	10.8 GB	Low	C43
NVFP4	4	12.3 GB	Medium	C44
Q4_K_M	4	13.4 GB	Medium	C44
Q5_K_M	5	15.8 GB	High	C45
Q6_K	6	18.0 GB	High	C45
Q8_0Best for your GPU	8	23.5 GB	Very High	C47
F16	16	45.1 GB	Maximum	F0

Frequently asked questions

Can Quadro RTX 8000 48GB run Codestral 22B v0.1?

Yes, Quadro RTX 8000 48GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 34.6 tok/s.

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

Codestral 22B v0.1 (22B parameters) requires approximately 22.0 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Codestral 22B v0.1 achieves approximately 34.6 tokens per second decode speed with a time-to-first-token of 5603ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on Quadro RTX 8000 48GB receives a C grade with 34.6 tok/s and 177K context.

What context window can Codestral 22B v0.1 use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Codestral 22B v0.1 can safely use up to 177K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.