Can Nemotron Nano 9B v2 run on Radeon PRO W7700 16GB?

YES — Runs Great

A84Great

Estimated from fit model

Nemotron Nano 9B v2 needs ~10.4 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q4_K_M quantization, expect ~67 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 10.4 GB, 66.5 tok/s, Runs well

10.4 GB required16.0 GB available

65% VRAM used

Fit status

Runs well

Decode

66.5 tok/s

TTFT

2909 ms

Safe context

52K

Memory

10.4 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on Radeon PRO W7700 16GB
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: 66.5 tok/s decode · 2.9s TTFT (warm) · 166 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	66.5 tok/s	1587 ms	52K
Coding	A	Runs well	66.5 tok/s	2909 ms	52K
Agentic Coding	S	Runs well	66.5 tok/s	4232 ms	52K
Reasoning	A	Runs well	66.5 tok/s	3438 ms	52K
RAG	S	Runs well	66.5 tok/s	5290 ms	52K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A77
Q3_K_S	3	4.4 GB	Low	A78
NVFP4	4	5.0 GB	Medium	A78
Q4_K_M	4	5.5 GB	Medium	A79
Q5_K_M	5	6.5 GB	High	A80
Q6_K	6	7.4 GB	High	A81
Q8_0Best for your GPU	8	9.6 GB	Very High	A81
F16	16	18.5 GB	Maximum	F0

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 Radeon PRO W7700 16GB can run

Model	Params	Grade	Decode
Qwen 3 14B	14B	S	43 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	40.7 tok/s
GPT-OSS 20B	21B	A	39.3 tok/s
Ministral 3 14B	14B	S	42.8 tok/s
Codestral 2 25.08	22B	A	14.4 tok/s

Frequently asked questions

Can Radeon PRO W7700 16GB run Nemotron Nano 9B v2?

Yes, Radeon PRO W7700 16GB can run Nemotron Nano 9B v2 with a A grade (Runs well). Expected decode speed: 66.5 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 9B v2?

The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 9B v2 run at on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Nemotron Nano 9B v2 achieves approximately 66.5 tokens per second decode speed with a time-to-first-token of 2909ms using Q4_K_M quantization.

Can Radeon PRO W7700 16GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on Radeon PRO W7700 16GB receives a A grade with 66.5 tok/s and 52K context.

What context window can Nemotron Nano 9B v2 use on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Nemotron Nano 9B v2 can safely use up to 52K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon PRO W7700 16GB See all hardware for Nemotron Nano 9B v2

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