Can Mistral Small 24B run on NVIDIA H100 80GB?

YES — Runs Great

A81Great

Estimated from fit model

Mistral Small 24B needs ~26.3 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~207 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) — 26.3 GB, 206.6 tok/s, Runs well

26.3 GB required80.0 GB available

33% VRAM used

Fit status

Runs well

Decode

206.6 tok/s

TTFT

937 ms

Safe context

33K

Memory

26.3 GB / 80.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsMistral Small 24B 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: 206.6 tok/s decode · 937ms TTFT (warm) · 517 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	A	Runs well	206.6 tok/s	511 ms	33K
Coding	A	Runs well	206.6 tok/s	937 ms	33K
Agentic Coding	A	Runs well	206.6 tok/s	1363 ms	33K
Reasoning	A	Runs well	206.6 tok/s	1107 ms	33K
RAG	A	Runs well	206.6 tok/s	1704 ms	33K

Quantization options

How Mistral Small 24B (24B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A72
Q3_K_S	3	11.8 GB	Low	A72
NVFP4	4	13.4 GB	Medium	A73
Q4_K_M	4	14.6 GB	Medium	A73
Q5_K_M	5	17.3 GB	High	A73
Q6_K	6	19.7 GB	High	A74
Q8_0	8	25.7 GB	Very High	A75
F16Best for your GPU	16	49.2 GB	Maximum	A80

Get started

Copy-paste commands to run Mistral Small 24B on your machine.

Run

ollama run mistral-small

Your hardware

More models your NVIDIA H100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	28.9 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	425.5 tok/s
Qwen 3.5 27B	27B	S	184.5 tok/s
Qwen 3.6 27B	27B	S	185.1 tok/s
Qwen 3.5 122B A10B	122B	S	85.5 tok/s

Frequently asked questions

Can NVIDIA H100 80GB run Mistral Small 24B?

Yes, NVIDIA H100 80GB can run Mistral Small 24B with a A grade (Runs well). Expected decode speed: 206.6 tok/s.

How much VRAM does Mistral Small 24B need?

Mistral Small 24B (24B parameters) requires approximately 26.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 24B?

The recommended quantization for Mistral Small 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 24B run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Mistral Small 24B achieves approximately 206.6 tokens per second decode speed with a time-to-first-token of 937ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Mistral Small 24B for coding?

For coding workloads, Mistral Small 24B on NVIDIA H100 80GB receives a A grade with 206.6 tok/s and 33K context.

What context window can Mistral Small 24B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Mistral Small 24B 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 Mistral Small 24B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

206.6 tok/s

511 ms

33K

Coding

Runs well

206.6 tok/s

937 ms

33K

Agentic Coding

Runs well

206.6 tok/s

1363 ms

33K

Reasoning

Runs well

206.6 tok/s

1107 ms

33K

RAG

Runs well

206.6 tok/s

1704 ms

33K

Quantization options

How Mistral Small 24B (24B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A72
Q3_K_S	3	11.8 GB	Low	A72
NVFP4	4	13.4 GB	Medium	A73
Q4_K_M	4	14.6 GB	Medium	A73
Q5_K_M	5	17.3 GB	High	A73
Q6_K	6	19.7 GB	High	A74
Q8_0	8	25.7 GB	Very High	A75
F16Best for your GPU	16	49.2 GB	Maximum	A80

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

28.9 tok/s

Qwen3-Coder 30B A3B Instruct

30.5B

425.5 tok/s

Qwen 3.5 27B

27B

184.5 tok/s

Qwen 3.6 27B

27B

185.1 tok/s

Qwen 3.5 122B A10B

122B

85.5 tok/s

Frequently asked questions

Can NVIDIA H100 80GB run Mistral Small 24B?

Yes, NVIDIA H100 80GB can run Mistral Small 24B with a A grade (Runs well). Expected decode speed: 206.6 tok/s.

How much VRAM does Mistral Small 24B need?

Mistral Small 24B (24B parameters) requires approximately 26.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 24B?

The recommended quantization for Mistral Small 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 24B run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Mistral Small 24B achieves approximately 206.6 tokens per second decode speed with a time-to-first-token of 937ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Mistral Small 24B for coding?

For coding workloads, Mistral Small 24B on NVIDIA H100 80GB receives a A grade with 206.6 tok/s and 33K context.

What context window can Mistral Small 24B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Mistral Small 24B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.