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URL: https://willitrunai.com/can-run/devstral-small-2507-on-rtx-pro-6000-blackwell-96gb

⇱ Devstral Small 1.1 on RTX PRO 6000 Blackwell Workstation Ed…


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 ~111 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well110.5 tok/s955 ms131K
CodingSRuns well110.5 tok/s1752 ms131K
Agentic CodingSRuns well110.5 tok/s2548 ms131K
ReasoningSRuns well110.5 tok/s2070 ms131K
RAGSRuns well110.5 tok/s3185 ms131K

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).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowA79
Q3_K_S
3
11.8 GB
LowA79
NVFP4
4
13.4 GB
MediumA79
Q4_K_M
4
14.6 GB
MediumA79
Q5_K_M
5
17.3 GB
HighA80
Q6_K
6
19.7 GB
HighA80
Q8_0
8
25.7 GB
Very HighA81
F16Best for your GPU
16
49.2 GB
MaximumS86

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

ModelParamsGradeDecodeCapabilities
👁 Mistral
Devstral 2 123B Instruct
123BS21.8 tok/s
👁 Alibaba
Qwen3-Coder 30B A3B Instruct
30.5BS227.6 tok/s
👁 Alibaba
Qwen 3.5 27B
27BS98.7 tok/s
👁 Alibaba
Qwen 3.6 27B
27BS99 tok/s
👁 Alibaba
Qwen 3.5 122B A10B
122BS60.5 tok/s

Frequently asked questions

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