Can Nemotron Nano 9B v2 run on Radeon Pro W7500 8GB?

YES — With NVFP4

B68Good

Estimated from fit model

Nemotron Nano 9B v2 needs ~9.2 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With NVFP4 quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

Nemotron Nano 9B v2 at Q4_K_M needs 9.6 GB — too much for Radeon Pro W7500 8GB (8.0 GB). Runs at NVFP4 (9.2 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.6 GB, exceeds 8.0 GB available

9.6 GB required8.0 GB available

120% VRAM needed

1.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.1 tok/s

TTFT

14742 ms

Safe context

Memory

9.6 GB / 8.0 GB

Offload

20%

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron Nano 9B v2 on Radeon Pro W7500 8GB
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: 13.1 tok/s decode · 14.7s TTFT (warm) · 33 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.3 GB host RAM)	17.5 tok/s	6046 ms	5K
Coding	F	Too heavy	13.1 tok/s	14742 ms	5K
Agentic Coding	F	Too heavy	8.2 tok/s	34501 ms	5K
Reasoning	F	Too heavy	13.1 tok/s	17423 ms	5K
RAG	F	Too heavy	8.2 tok/s	43127 ms	5K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A83
Q3_K_S	3	4.4 GB	Low	A83
NVFP4Best for your GPU	4	5.0 GB	Medium	A82
Q4_K_M	4	5.5 GB	Medium	F0
Q5_K_M	5	6.5 GB	High	F0
Q6_K	6	7.4 GB	High	F0
Q8_0	8	9.6 GB	Very High	F0
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

アップグレードオプション

Nemotron Nano 9B v2を快適に動かすハードウェア

RX 7600 XT 16GBコスパ重視

16 GB VRAM (+8)288 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.32.7 tok/sデコード

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

〜$329 MSRP

RX 9060 XT 16GBコスパ最良

16 GB VRAM (+8)320 GB/s (+96)

Makes the model fit on the accelerator instead of staying completely out of reach.39.5 tok/sデコード

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

〜$349 MSRP

RX 7700 XT 12GBAMDアップグレード

12 GB VRAM (+4)432 GB/s (+208)

Makes the model fit on the accelerator instead of staying completely out of reach.50.8 tok/sデコード

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

〜$449 MSRP

Frequently asked questions

Can Radeon Pro W7500 8GB run Nemotron Nano 9B v2?

Yes, Radeon Pro W7500 8GB can run Nemotron Nano 9B v2 at NVFP4 quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 9.6 GB which exceeds available memory, but at NVFP4 it needs only 9.2 GB. Expected decode speed: 16.6 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 9.6 GB at Q4_K_M quantization. On Radeon Pro W7500 8GB, it fits at NVFP4 using 9.2 GB.

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

The recommended quantization is Q4_K_M, but on Radeon Pro W7500 8GB the best fitting quantization is NVFP4, which uses 9.2 GB.

What speed will Nemotron Nano 9B v2 run at on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, Nemotron Nano 9B v2 achieves approximately 16.6 tokens per second decode speed with a time-to-first-token of 11655ms using NVFP4 quantization.

Can Radeon Pro W7500 8GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on Radeon Pro W7500 8GB receives a F grade with 13.1 tok/s and 5K context.

What context window can Nemotron Nano 9B v2 use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, Nemotron Nano 9B v2 can safely use up to 8K tokens of context at NVFP4 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron Nano 9B v2 feels slow on Radeon Pro W7500 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for Radeon Pro W7500 8GB See all hardware for Nemotron Nano 9B v2

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