Can Devstral 2 123B Instruct run on NVIDIA B200 180GB?

YES — Runs Great

S97Excellent

○Estimated from fit model

Devstral 2 123B Instruct needs ~99.3 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~97 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 99.3 GB, 97.4 tok/s, Runs well

99.3 GB required180.0 GB available

55% VRAM used

Fit status

Runs well

Decode

97.4 tok/s

TTFT

1988 ms

Safe context

256K

Memory

99.3 GB / 180.0 GB

Memory breakdown

Weights75.0 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsDevstral 2 123B Instruct on NVIDIA B200 180GB
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: 97.4 tok/s decode · 2.0s TTFT (warm) · 244 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	97.4 tok/s	1084 ms	256K
Coding	S	Runs well	97.4 tok/s	1988 ms	256K
Agentic Coding	S	Runs well	97.4 tok/s	2891 ms	256K
Reasoning	S	Runs well	97.4 tok/s	2349 ms	256K
RAG	S	Runs well	97.4 tok/s	3614 ms	256K

Quantization options

How Devstral 2 123B Instruct (123B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	48.0 GB	Low	A85
Q3_K_S	3	60.3 GB	Low	S86
NVFP4	4

Get started

Copy-paste commands to run Devstral 2 123B Instruct on your machine.

Run

lms load Devstral-2-123B-Instruct-2512 && lms server start

Frequently asked questions

See all results for NVIDIA B200 180GB See all hardware for Devstral 2 123B Instruct

URL: https://willitrunai.com/can-run/devstral-2-123b-on-b200-180gb

⇱