Can Devstral Small 2 24B Instruct run on RTX PRO 6000 Blackwell Server Edition 96GB?

YES — Runs Great

S89Excellent

Estimated from fit model

Devstral Small 2 24B Instruct needs ~27.9 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~99 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) — 27.9 GB, 98.5 tok/s, Runs well

27.9 GB required96.0 GB available

29% VRAM used

Fit status

Runs well

Decode

98.5 tok/s

TTFT

1965 ms

Safe context

256K

Memory

27.9 GB / 96.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsDevstral Small 2 24B Instruct on RTX PRO 6000 Blackwell Server Edition 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: 98.5 tok/s decode · 2.0s TTFT (warm) · 246 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	98.5 tok/s	1072 ms	256K
Coding	S	Runs well	98.5 tok/s	1965 ms	256K
Agentic Coding	S	Runs well	91.6 tok/s	3073 ms	256K
Reasoning	S	Runs well	98.5 tok/s	2323 ms	256K
RAG	S	Runs well	98.5 tok/s	3573 ms	256K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A81
Q3_K_S	3	11.8 GB	Low	A81
NVFP4	4	13.4 GB	Medium	A81
Q4_K_M	4	14.6 GB	Medium	A81
Q5_K_M	5	17.3 GB	High	A82
Q6_K	6	19.7 GB	High	A82
Q8_0	8	25.7 GB	Very High	A83
F16Best for your GPU	16	49.2 GB	Maximum	S88

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 RTX PRO 6000 Blackwell Server Edition 96GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	19.4 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	202.8 tok/s
Qwen 3.5 27B	27B	S	88 tok/s
Qwen 3.6 27B	27B	S	88.2 tok/s
Qwen 3.5 122B A10B	122B	S	53.9 tok/s

Frequently asked questions

Can RTX PRO 6000 Blackwell Server Edition 96GB run Devstral Small 2 24B Instruct?

Yes, RTX PRO 6000 Blackwell Server Edition 96GB can run Devstral Small 2 24B Instruct with a S grade (Runs well). Expected decode speed: 98.5 tok/s.

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

Devstral Small 2 24B Instruct (24B parameters) requires approximately 27.9 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 RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, Devstral Small 2 24B Instruct achieves approximately 98.5 tokens per second decode speed with a time-to-first-token of 1965ms using Q4_K_M quantization.

Can RTX PRO 6000 Blackwell Server Edition 96GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on RTX PRO 6000 Blackwell Server Edition 96GB receives a S grade with 98.5 tok/s and 256K context.

What context window can Devstral Small 2 24B Instruct use on RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, 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 RTX PRO 6000 Blackwell Server Edition 96GB See all hardware for Devstral Small 2 24B Instruct

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