Can Codestral 22B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

B59Good

Estimated from fit model

Codestral 22B needs ~25.1 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~135 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, 134.6 tok/s, Runs well

25.1 GB required80.0 GB available

31% VRAM used

Fit status

Runs well

Decode

134.6 tok/s

TTFT

1439 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 PCIe 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: 134.6 tok/s decode · 1.4s TTFT (warm) · 336 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	125.2 tok/s	844 ms	33K
Coding	B	Runs well	134.6 tok/s	1439 ms	33K
Agentic Coding	B	Runs well	134.6 tok/s	2093 ms	33K
Reasoning	B	Runs well	134.6 tok/s	1700 ms	33K
RAG	B	Runs well	134.6 tok/s	2616 ms	33K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on NVIDIA H100 PCIe 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 PCIe 80GB run Codestral 22B?

Yes, NVIDIA H100 PCIe 80GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 134.6 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 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Codestral 22B achieves approximately 134.6 tokens per second decode speed with a time-to-first-token of 1439ms using Q4_K_M quantization.

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

For coding workloads, Codestral 22B on NVIDIA H100 PCIe 80GB receives a B grade with 134.6 tok/s and 33K context.

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

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

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