Can Codestral 22B run on NVIDIA H100 80GB?

YES — Runs Great

B59Good

Estimated from fit model

Codestral 22B needs ~25.1 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~225 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) — 25.1 GB, 225.4 tok/s, Runs well

25.1 GB required80.0 GB available

31% VRAM used

Fit status

Runs well

Decode

225.4 tok/s

TTFT

859 ms

Safe context

33K

Memory

25.1 GB / 80.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsCodestral 22B on NVIDIA H100 80GB
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: 225.4 tok/s decode · 859ms TTFT (warm) · 564 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	225.4 tok/s	468 ms	33K
Coding	B	Runs well	225.4 tok/s	859 ms	33K
Agentic Coding	B	Runs well	225.4 tok/s	1249 ms	33K
Reasoning	B	Runs well	225.4 tok/s	1015 ms	33K
RAG	B	Runs well	225.4 tok/s	1562 ms	33K

Inference speed

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

Estimated decode speed (tokens/sec) for Codestral 22B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~96 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	96.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	61.4	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	55.4	Fits
RTX 3090 24GB	24 GB	Q4_K_M	52.5	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	44.6	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	37.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	37.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	37.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	35.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	26.5	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	19.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	17.6	Fits
RTX 4070 12GB	12 GB	Q4_K_M	9.4	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.9	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.4	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 (22B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C50
Q3_K_S	3	10.8 GB	Low	C50
NVFP4	4	12.3 GB	Medium	C51
Q4_K_M	4	13.4 GB	Medium	C51
Q5_K_M	5	15.8 GB	High	C51
Q6_K	6	18.0 GB	High	C51
Q8_0	8	23.5 GB	Very High	C52
F16Best for your GPU	16	45.1 GB	Maximum	B58

Get started

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

Run

ollama run codestral

Frequently asked questions

Can NVIDIA H100 80GB run Codestral 22B?

Yes, NVIDIA H100 80GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 225.4 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 25.1 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 NVIDIA H100 80GB?

On NVIDIA H100 80GB, Codestral 22B achieves approximately 225.4 tokens per second decode speed with a time-to-first-token of 859ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on NVIDIA H100 80GB receives a B grade with 225.4 tok/s and 33K context.

What context window can Codestral 22B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, 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 NVIDIA H100 80GB See all hardware for Codestral 22B

Embed this result▼

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

225.4 tok/s

468 ms

33K

Coding

Runs well

225.4 tok/s

859 ms

33K

Agentic Coding

Runs well

225.4 tok/s

1249 ms

33K

Reasoning

Runs well

225.4 tok/s

1015 ms

33K

RAG

Runs well

225.4 tok/s

1562 ms

33K

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

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

96.2

Fits

RTX 4090 24GB

24 GB

Q4_K_M

61.4

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

55.4

Fits

RTX 3090 24GB

24 GB

Q4_K_M

52.5

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

44.6

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

37.4

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

37.4

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

37.2

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

35.2

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

26.5

Heavy offload

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

23.6

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

19.2

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

17.6

Fits

RTX 4070 12GB

12 GB

Q4_K_M

9.4

Too big

RTX 3060 12GB

12 GB

Q4_K_M

5.9

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.4

Too big

Quantization options

How Codestral 22B (22B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C50
Q3_K_S	3	10.8 GB	Low	C50
NVFP4	4	12.3 GB	Medium	C51
Q4_K_M	4	13.4 GB	Medium	C51
Q5_K_M	5	15.8 GB	High	C51
Q6_K	6	18.0 GB	High	C51
Q8_0	8	23.5 GB	Very High	C52
F16Best for your GPU	16	45.1 GB	Maximum	B58

Frequently asked questions

Can NVIDIA H100 80GB run Codestral 22B?

Yes, NVIDIA H100 80GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 225.4 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 25.1 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 NVIDIA H100 80GB?

On NVIDIA H100 80GB, Codestral 22B achieves approximately 225.4 tokens per second decode speed with a time-to-first-token of 859ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on NVIDIA H100 80GB receives a B grade with 225.4 tok/s and 33K context.

What context window can Codestral 22B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, 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.