Can Codestral 22B v0.1 i1 run on NVIDIA GH200 96GB?

YES — Runs Great

C47Usable

Estimated from fit model

Codestral 22B v0.1 i1 needs ~26.8 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~241 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) — 26.8 GB, 241.4 tok/s, Runs well

26.8 GB required96.0 GB available

28% VRAM used

Fit status

Runs well

Decode

241.4 tok/s

TTFT

802 ms

Safe context

445K

Memory

26.8 GB / 96.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 i1 on NVIDIA GH200 96GB
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: 241.4 tok/s decode · 802ms TTFT (warm) · 604 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	241.4 tok/s	437 ms	445K
Coding	C	Runs well	241.4 tok/s	802 ms	445K
Agentic Coding	C	Runs well	241.4 tok/s	1166 ms	445K
Reasoning	C	Runs well	241.4 tok/s	948 ms	445K
RAG	C	Runs well	241.4 tok/s	1458 ms	445K

Inference speed

Codestral 22B v0.1 i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral 22B v0.1 i1 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 i1 (22B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	D39
Q3_K_S	3	10.8 GB	Low	D39
NVFP4	4	12.3 GB	Medium	D39
Q4_K_M	4	13.4 GB	Medium	D39
Q5_K_M	5	15.8 GB	High	D40
Q6_K	6	18.0 GB	High	D40
Q8_0	8	23.5 GB	Very High	C41
F16Best for your GPU	16	45.1 GB	Maximum	C45

Get started

Copy-paste commands to run Codestral 22B v0.1 i1 on your machine.

Run

lms load hf-mradermacher--codestral-22b-v0-1-i1-gguf && lms server start

Frequently asked questions

Can NVIDIA GH200 96GB run Codestral 22B v0.1 i1?

Yes, NVIDIA GH200 96GB can run Codestral 22B v0.1 i1 with a C grade (Runs well). Expected decode speed: 241.4 tok/s.

How much VRAM does Codestral 22B v0.1 i1 need?

Codestral 22B v0.1 i1 (22B parameters) requires approximately 26.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1 i1?

The recommended quantization for Codestral 22B v0.1 i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 i1 run at on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, Codestral 22B v0.1 i1 achieves approximately 241.4 tokens per second decode speed with a time-to-first-token of 802ms using Q4_K_M quantization.

Can NVIDIA GH200 96GB run Codestral 22B v0.1 i1 for coding?

For coding workloads, Codestral 22B v0.1 i1 on NVIDIA GH200 96GB receives a C grade with 241.4 tok/s and 445K context.

What context window can Codestral 22B v0.1 i1 use on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, Codestral 22B v0.1 i1 can safely use up to 445K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA GH200 96GB See all hardware for Codestral 22B v0.1 i1

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

241.4 tok/s

437 ms

445K

Coding

Runs well

241.4 tok/s

802 ms

445K

Agentic Coding

Runs well

241.4 tok/s

1166 ms

445K

Reasoning

Runs well

241.4 tok/s

948 ms

445K

RAG

Runs well

241.4 tok/s

1458 ms

445K

Codestral 22B v0.1 i1 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 i1 (22B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	D39
Q3_K_S	3	10.8 GB	Low	D39
NVFP4	4	12.3 GB	Medium	D39
Q4_K_M	4	13.4 GB	Medium	D39
Q5_K_M	5	15.8 GB	High	D40
Q6_K	6	18.0 GB	High	D40
Q8_0	8	23.5 GB	Very High	C41
F16Best for your GPU	16	45.1 GB	Maximum	C45

Frequently asked questions

Can NVIDIA GH200 96GB run Codestral 22B v0.1 i1?

Yes, NVIDIA GH200 96GB can run Codestral 22B v0.1 i1 with a C grade (Runs well). Expected decode speed: 241.4 tok/s.

How much VRAM does Codestral 22B v0.1 i1 need?

Codestral 22B v0.1 i1 (22B parameters) requires approximately 26.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1 i1?

The recommended quantization for Codestral 22B v0.1 i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 i1 run at on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, Codestral 22B v0.1 i1 achieves approximately 241.4 tokens per second decode speed with a time-to-first-token of 802ms using Q4_K_M quantization.

Can NVIDIA GH200 96GB run Codestral 22B v0.1 i1 for coding?

For coding workloads, Codestral 22B v0.1 i1 on NVIDIA GH200 96GB receives a C grade with 241.4 tok/s and 445K context.

What context window can Codestral 22B v0.1 i1 use on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, Codestral 22B v0.1 i1 can safely use up to 445K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.