Can Devstral 2 123B Instruct run on AMD Instinct MI325X 256GB?

YES — Runs Great

S93Excellent

○Estimated from fit model

Devstral 2 123B Instruct needs ~106.9 GB VRAM. AMD Instinct MI325X 256GB has 256.0 GB. With Q4_K_M quantization, expect ~58 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) — 106.9 GB, 63.5 tok/s, Runs well

106.9 GB required256.0 GB available

42% VRAM used

Fit status

Runs well

Decode

63.5 tok/s

TTFT

3050 ms

Safe context

256K

Memory

106.9 GB / 256.0 GB

Memory breakdown

Weights75.0 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom25.6 GB

See how fast it feels

See how fast it feelsDevstral 2 123B Instruct on AMD Instinct MI325X 256GB
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: 63.5 tok/s decode · 3.0s TTFT (warm) · 159 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	58.4 tok/s	1809 ms	256K
Coding	S	Runs well	58.4 tok/s	3316 ms	256K
Agentic Coding	S	Runs well	58.4 tok/s	4824 ms	256K
Reasoning	S	Runs well	58.4 tok/s	3919 ms	256K
RAG	S	Runs well	58.4 tok/s	6030 ms	256K

Quantization options

How Devstral 2 123B Instruct (123B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	48.0 GB	Low	A83
Q3_K_S	3	60.3 GB	Low	A84
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

Your hardware

More models your AMD Instinct MI325X 256GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 397B A17B	397B	A	39.2 tok/s

Frequently asked questions

See all results for AMD Instinct MI325X 256GB See all hardware for Devstral 2 123B Instruct

URL: https://willitrunai.com/can-run/devstral-2-123b-on-instinct-mi325x-256gb

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