Can Devstral Small 2 24B Instruct run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Devstral Small 2 24B Instruct needs ~26.3 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~123 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, 123.4 tok/s, Runs well

26.3 GB required80.0 GB available

33% VRAM used

Fit status

Runs well

Decode

123.4 tok/s

TTFT

1569 ms

Safe context

256K

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 feelsDevstral Small 2 24B Instruct 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: 123.4 tok/s decode · 1.6s TTFT (warm) · 308 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	S	Runs well	123.4 tok/s	856 ms	256K
Coding	S	Runs well	123.4 tok/s	1569 ms	256K
Agentic Coding	S	Runs well	123.4 tok/s	2283 ms	256K
Reasoning	S	Runs well	123.4 tok/s	1855 ms	256K
RAG	S	Runs well	123.4 tok/s	2853 ms	256K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A81
Q3_K_S	3	11.8 GB	Low	A82
NVFP4	4	13.4 GB	Medium	A82
Q4_K_M	4	14.6 GB	Medium	A82
Q5_K_M	5	17.3 GB	High	A83
Q6_K	6	19.7 GB	High	A83
Q8_0	8	25.7 GB	Very High	A84
F16Best for your GPU	16	49.2 GB	Maximum	S89

Get started

Copy-paste commands to run Devstral Small 2 24B Instruct on your machine.

Run

ollama run devstral-small-2

Your hardware

More models your NVIDIA H100 PCIe 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	14.8 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	254 tok/s
Qwen 3.5 27B	27B	S	110.2 tok/s
Qwen 3.6 27B	27B	S	110.5 tok/s
Qwen 3.5 122B A10B	122B	A	44.5 tok/s

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Devstral Small 2 24B Instruct?

Yes, NVIDIA H100 PCIe 80GB can run Devstral Small 2 24B Instruct with a S grade (Runs well). Expected decode speed: 123.4 tok/s.

How much VRAM does Devstral Small 2 24B Instruct need?

Devstral Small 2 24B Instruct (24B parameters) requires approximately 26.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral Small 2 24B Instruct?

The recommended quantization for Devstral Small 2 24B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral Small 2 24B Instruct run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Devstral Small 2 24B Instruct achieves approximately 123.4 tokens per second decode speed with a time-to-first-token of 1569ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on NVIDIA H100 PCIe 80GB receives a S grade with 123.4 tok/s and 256K context.

What context window can Devstral Small 2 24B Instruct use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Devstral Small 2 24B Instruct can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA H100 PCIe 80GB See all hardware for Devstral Small 2 24B Instruct

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

123.4 tok/s

856 ms

256K

Coding

Runs well

123.4 tok/s

1569 ms

256K

Agentic Coding

Runs well

123.4 tok/s

2283 ms

256K

Reasoning

Runs well

123.4 tok/s

1855 ms

256K

RAG

Runs well

123.4 tok/s

2853 ms

256K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A81
Q3_K_S	3	11.8 GB	Low	A82
NVFP4	4	13.4 GB	Medium	A82
Q4_K_M	4	14.6 GB	Medium	A82
Q5_K_M	5	17.3 GB	High	A83
Q6_K	6	19.7 GB	High	A83
Q8_0	8	25.7 GB	Very High	A84
F16Best for your GPU	16	49.2 GB	Maximum	S89

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

14.8 tok/s

Qwen3-Coder 30B A3B Instruct

30.5B

254 tok/s

Qwen 3.5 27B

27B

110.2 tok/s

Qwen 3.6 27B

27B

110.5 tok/s

Qwen 3.5 122B A10B

122B

44.5 tok/s