Can Nemotron Nano 9B v2 run on AMD Instinct MI250 128GB?

YES — Runs Great

A75Great

○Estimated from fit model

Nemotron Nano 9B v2 needs ~21.6 GB VRAM. AMD Instinct MI250 128GB has 128.0 GB. With Q4_K_M quantization, expect ~126 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) — 21.6 GB, 126.0 tok/s, Runs well

21.6 GB required128.0 GB available

17% VRAM used

Fit status

Runs well

Decode

126.0 tok/s

TTFT

1537 ms

Safe context

131K

Memory

21.6 GB / 128.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on AMD Instinct MI250 128GB
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: 126.0 tok/s decode · 1.5s TTFT (warm) · 315 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	A	Runs well	126.0 tok/s	838 ms	131K
Coding	A	Runs well	126.0 tok/s	1537 ms	131K
Agentic Coding	A	Runs well	126.0 tok/s	2235 ms	131K
Reasoning	A	Runs well	126.0 tok/s	1816 ms	131K
RAG	A	Runs well	126.0 tok/s	2794 ms	131K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on AMD Instinct MI250 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B68
Q3_K_S	3	4.4 GB	Low	B68
NVFP4	4	5.0 GB	Medium	B68
Q4_K_M	4	5.5 GB	Medium	B68
Q5_K_M	5	6.5 GB	High	B68
Q6_K	6	7.4 GB	High	B68
Q8_0	8	9.6 GB	Very High	B68
F16Best for your GPU	16	18.5 GB	Maximum	B68

Get started

Copy-paste commands to run Nemotron Nano 9B v2 on your machine.

Run

ollama run nemotron-nano:9b-v2

Your hardware

More models your AMD Instinct MI250 128GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	31.5 tok/s
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	329 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	142.7 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	88.9 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	87.5 tok/s

Frequently asked questions

See all results for AMD Instinct MI250 128GB See all hardware for Nemotron Nano 9B v2

URL: https://willitrunai.com/can-run/nemotron-nano-9b-v2-on-instinct-mi250-128gb

⇱ Nemotron Nano 9B v2 on AMD Instinct MI250 128GB? YES