Can Codestral 22B run on NVIDIA H20 96GB?

YES — Runs Great

B58Good

Estimated from fit model

Codestral 22B needs ~26.7 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~260 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) — 26.7 GB, 259.5 tok/s, Runs well

26.7 GB required96.0 GB available

28% VRAM used

Fit status

Runs well

Decode

259.5 tok/s

TTFT

746 ms

Safe context

33K

Memory

26.7 GB / 96.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsCodestral 22B on NVIDIA H20 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: 259.5 tok/s decode · 746ms TTFT (warm) · 649 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	259.5 tok/s	407 ms	33K
Coding	B	Runs well	259.5 tok/s	746 ms	33K
Agentic Coding	B	Runs well	259.5 tok/s	1085 ms	33K
Reasoning	B	Runs well	259.5 tok/s	882 ms	33K
RAG	B	Runs well	259.5 tok/s	1356 ms	33K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C49
Q3_K_S	3	10.8 GB	Low	C50
NVFP4	4	12.3 GB	Medium	C50
Q4_K_M	4	13.4 GB	Medium	C50
Q5_K_M	5	15.8 GB	High	C50
Q6_K	6	18.0 GB	High	C50
Q8_0	8	23.5 GB	Very High	C51
F16Best for your GPU	16	45.1 GB	Maximum	B56

Get started

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

Run

ollama run codestral

Frequently asked questions

Can NVIDIA H20 96GB run Codestral 22B?

Yes, NVIDIA H20 96GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 259.5 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 26.7 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 H20 96GB?

On NVIDIA H20 96GB, Codestral 22B achieves approximately 259.5 tokens per second decode speed with a time-to-first-token of 746ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on NVIDIA H20 96GB receives a B grade with 259.5 tok/s and 33K context.

What context window can Codestral 22B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, 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 H20 96GB See all hardware for Codestral 22B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

259.5 tok/s

407 ms

33K

Coding

Runs well

259.5 tok/s

746 ms

33K

Agentic Coding

Runs well

259.5 tok/s

1085 ms

33K

Reasoning

Runs well

259.5 tok/s

882 ms

33K

RAG

Runs well

259.5 tok/s

1356 ms

33K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C49
Q3_K_S	3	10.8 GB	Low	C50
NVFP4	4	12.3 GB	Medium	C50
Q4_K_M	4	13.4 GB	Medium	C50
Q5_K_M	5	15.8 GB	High	C50
Q6_K	6	18.0 GB	High	C50
Q8_0	8	23.5 GB	Very High	C51
F16Best for your GPU	16	45.1 GB	Maximum	B56

Frequently asked questions

Can NVIDIA H20 96GB run Codestral 22B?

Yes, NVIDIA H20 96GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 259.5 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 26.7 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 H20 96GB?

On NVIDIA H20 96GB, Codestral 22B achieves approximately 259.5 tokens per second decode speed with a time-to-first-token of 746ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on NVIDIA H20 96GB receives a B grade with 259.5 tok/s and 33K context.

What context window can Codestral 22B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, 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.