Can Codestral 22B v0.1 run on NVIDIA V100 32GB?

YES — Runs Great

C53Usable

Estimated from fit model

Codestral 22B v0.1 needs ~20.4 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~45 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) — 20.4 GB, 44.9 tok/s, Runs well

20.4 GB required32.0 GB available

64% VRAM used

Fit status

Runs well

Decode

44.9 tok/s

TTFT

4309 ms

Safe context

88K

Memory

20.4 GB / 32.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 on NVIDIA V100 32GB
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: 44.9 tok/s decode · 4.3s TTFT (warm) · 112 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	C	Runs well	44.9 tok/s	2350 ms	88K
Coding	C	Runs well	44.9 tok/s	4309 ms	88K
Agentic Coding	C	Runs well	44.9 tok/s	6267 ms	88K
Reasoning	C	Runs well	44.9 tok/s	5092 ms	88K
RAG	C	Runs well	44.9 tok/s	7834 ms	88K

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 NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C45
Q3_K_S	3	10.8 GB	Low	C46
NVFP4	4	12.3 GB	Medium	C47
Q4_K_M	4	13.4 GB	Medium	C47
Q5_K_M	5	15.8 GB	High	C49
Q6_K	6	18.0 GB	High	C49
Q8_0Best for your GPU	8	23.5 GB	Very High	C48
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-sanctumai--codestral-22b-v0-1-gguf && lms server start

Frequently asked questions

Can NVIDIA V100 32GB run Codestral 22B v0.1?

Yes, NVIDIA V100 32GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 44.9 tok/s.

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

Codestral 22B v0.1 (22B parameters) requires approximately 20.4 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 NVIDIA V100 32GB?

On NVIDIA V100 32GB, Codestral 22B v0.1 achieves approximately 44.9 tokens per second decode speed with a time-to-first-token of 4309ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on NVIDIA V100 32GB receives a C grade with 44.9 tok/s and 88K context.

What context window can Codestral 22B v0.1 use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Codestral 22B v0.1 can safely use up to 88K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Codestral 22B v0.1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-sanctumai--codestral-22b-v0-1-gguf-on-v100-32gb" 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

44.9 tok/s

2350 ms

88K

Coding

Runs well

44.9 tok/s

4309 ms

88K

Agentic Coding

Runs well

44.9 tok/s

6267 ms

88K

Reasoning

Runs well

44.9 tok/s

5092 ms

88K

RAG

Runs well

44.9 tok/s

7834 ms

88K

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 NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C45
Q3_K_S	3	10.8 GB	Low	C46
NVFP4	4	12.3 GB	Medium	C47
Q4_K_M	4	13.4 GB	Medium	C47
Q5_K_M	5	15.8 GB	High	C49
Q6_K	6	18.0 GB	High	C49
Q8_0Best for your GPU	8	23.5 GB	Very High	C48
F16	16	45.1 GB	Maximum	F0

Frequently asked questions

Can NVIDIA V100 32GB run Codestral 22B v0.1?

Yes, NVIDIA V100 32GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 44.9 tok/s.

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

Codestral 22B v0.1 (22B parameters) requires approximately 20.4 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 NVIDIA V100 32GB?

On NVIDIA V100 32GB, Codestral 22B v0.1 achieves approximately 44.9 tokens per second decode speed with a time-to-first-token of 4309ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on NVIDIA V100 32GB receives a C grade with 44.9 tok/s and 88K context.

What context window can Codestral 22B v0.1 use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Codestral 22B v0.1 can safely use up to 88K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.