Can Devstral Small 2 24B Instruct run on NVIDIA A100 40GB?

YES — Runs Great

S95Excellent

Estimated from fit model

Devstral Small 2 24B Instruct needs ~22.3 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~96 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) — 22.3 GB, 95.9 tok/s, Runs well

22.3 GB required40.0 GB available

56% VRAM used

Fit status

Runs well

Decode

95.9 tok/s

TTFT

2018 ms

Safe context

132K

Memory

22.3 GB / 40.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsDevstral Small 2 24B Instruct on NVIDIA A100 40GB
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: 95.9 tok/s decode · 2.0s TTFT (warm) · 240 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	95.9 tok/s	1101 ms	132K
Coding	S	Runs well	95.9 tok/s	2018 ms	132K
Agentic Coding	S	Runs well	95.9 tok/s	2936 ms	132K
Reasoning	S	Runs well	95.9 tok/s	2385 ms	132K
RAG	S	Runs well	95.9 tok/s	3670 ms	132K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S85
Q3_K_S	3	11.8 GB	Low	S86
NVFP4	4	13.4 GB	Medium	S87
Q4_K_M	4	14.6 GB	Medium	S87
Q5_K_M	5	17.3 GB	High	S88
Q6_K	6	19.7 GB	High	S89
Q8_0Best for your GPU	8	25.7 GB	Very High	S90
F16	16	49.2 GB	Maximum	F0

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 A100 40GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
Qwen 3.5 27B	27B	S	85.7 tok/s
Qwen 3.6 27B	27B	S	85.9 tok/s
Qwen 3.6 35B A3B	35B	S	166 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	204.3 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run Devstral Small 2 24B Instruct?

Yes, NVIDIA A100 40GB can run Devstral Small 2 24B Instruct with a S grade (Runs well). Expected decode speed: 95.9 tok/s.

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

Devstral Small 2 24B Instruct (24B parameters) requires approximately 22.3 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 A100 40GB?

On NVIDIA A100 40GB, Devstral Small 2 24B Instruct achieves approximately 95.9 tokens per second decode speed with a time-to-first-token of 2018ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on NVIDIA A100 40GB receives a S grade with 95.9 tok/s and 132K context.

What context window can Devstral Small 2 24B Instruct use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Devstral Small 2 24B Instruct can safely use up to 132K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Devstral Small 2 24B Instruct

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

95.9 tok/s

1101 ms

132K

Coding

Runs well

95.9 tok/s

2018 ms

132K

Agentic Coding

Runs well

95.9 tok/s

2936 ms

132K

Reasoning

Runs well

95.9 tok/s

2385 ms

132K

RAG

Runs well

95.9 tok/s

3670 ms

132K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S85
Q3_K_S	3	11.8 GB	Low	S86
NVFP4	4	13.4 GB	Medium	S87
Q4_K_M	4	14.6 GB	Medium	S87
Q5_K_M	5	17.3 GB	High	S88
Q6_K	6	19.7 GB	High	S89
Q8_0Best for your GPU	8	25.7 GB	Very High	S90
F16	16	49.2 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

197.5 tok/s

Qwen 3.5 27B

27B

85.7 tok/s

Qwen 3.6 27B

27B

85.9 tok/s

Qwen 3.6 35B A3B

35B

166 tok/s

Qwen3-VL 30B A3B Instruct

30B

204.3 tok/s