Can Nemotron 3 Nano 30B run on AMD Instinct MI60 32GB?

YES — Runs Great

S93Excellent

○Estimated from fit model

Nemotron 3 Nano 30B needs ~24.8 GB VRAM. AMD Instinct MI60 32GB has 32.0 GB. With Q4_K_M quantization, expect ~27 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) — 24.8 GB, 29.5 tok/s, Runs well

24.8 GB required32.0 GB available

78% VRAM used

Fit status

Runs well

Decode

29.5 tok/s

TTFT

6568 ms

Safe context

63K

Memory

24.8 GB / 32.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B on AMD Instinct MI60 32GB
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: 29.5 tok/s decode · 6.6s TTFT (warm) · 74 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	29.5 tok/s	3583 ms	63K
Coding	S	Runs well	27.4 tok/s	7061 ms	63K
Agentic Coding	S	Tight fit	29.5 tok/s	9554 ms	63K
Reasoning	S	Runs well	29.5 tok/s	7763 ms	63K
RAG	S	Tight fit	29.5 tok/s	11942 ms	63K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on AMD Instinct MI60 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S87
Q3_K_S	3	14.7 GB	Low	S89
NVFP4	4

Get started

Copy-paste commands to run Nemotron 3 Nano 30B on your machine.

Run

ollama run nemotron-nano:30b

Your hardware

More models your AMD Instinct MI60 32GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	75.9 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	63.8 tok/s

Frequently asked questions

See all results for AMD Instinct MI60 32GB See all hardware for Nemotron 3 Nano 30B

URL: https://willitrunai.com/can-run/nemotron-3-nano-30b-on-instinct-mi60-32gb

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