Can Devstral Small 1.1 run on RTX PRO 6000 Blackwell Workstation Edition 96GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Devstral Small 1.1 needs ~27.9 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~103 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, 110.5 tok/s, Runs well

27.9 GB required96.0 GB available

29% VRAM used

Fit status

Runs well

Decode

110.5 tok/s

TTFT

1752 ms

Safe context

131K

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 1.1 on RTX PRO 6000 Blackwell Workstation 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: 110.5 tok/s decode · 1.8s TTFT (warm) · 276 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	102.8 tok/s	1027 ms	131K
Coding	S	Runs well	102.8 tok/s	1883 ms	131K
Agentic Coding	S	Runs well	102.8 tok/s	2739 ms	131K
Reasoning	S	Runs well	102.8 tok/s	2225 ms	131K
RAG	S	Runs well	102.8 tok/s	3423 ms	131K

Quantization options

How Devstral Small 1.1 (24B params) fits at each quantization level on RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A79
Q3_K_S	3	11.8 GB	Low	A79
NVFP4	4

Get started

Copy-paste commands to run Devstral Small 1.1 on your machine.

Run

lms load Devstral-Small-2507 && lms server start

Your hardware

More models your RTX PRO 6000 Blackwell Workstation Edition 96GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	21.8 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B

Frequently asked questions

Can RTX PRO 6000 Blackwell Workstation Edition 96GB run Devstral Small 1.1?

Yes, RTX PRO 6000 Blackwell Workstation Edition 96GB can run Devstral Small 1.1 with a S grade (Runs well). Expected decode speed: 102.8 tok/s.

How much VRAM does Devstral Small 1.1 need?

Devstral Small 1.1 (24B parameters) requires approximately 27.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral Small 1.1?

The recommended quantization for Devstral Small 1.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral Small 1.1 run at on RTX PRO 6000 Blackwell Workstation Edition 96GB?

On RTX PRO 6000 Blackwell Workstation Edition 96GB, Devstral Small 1.1 achieves approximately 102.8 tokens per second decode speed with a time-to-first-token of 1883ms using Q4_K_M quantization.

Can RTX PRO 6000 Blackwell Workstation Edition 96GB run Devstral Small 1.1 for coding?

For coding workloads, Devstral Small 1.1 on RTX PRO 6000 Blackwell Workstation Edition 96GB receives a S grade with 102.8 tok/s and 131K context.

What context window can Devstral Small 1.1 use on RTX PRO 6000 Blackwell Workstation Edition 96GB?

On RTX PRO 6000 Blackwell Workstation Edition 96GB, Devstral Small 1.1 can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX PRO 6000 Blackwell Workstation Edition 96GB See all hardware for Devstral Small 1.1

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