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

Q: What should I upgrade first if Nemotron Nano 8B 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.

YES — With Offload

A74Great

Estimated from fit model

Nemotron Nano 8B needs ~8.5 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~18 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.5 GB, 19.1 tok/s, Runs with offload (needs ~0.3 GB host RAM)

8.5 GB required8.0 GB available

106% VRAM needed

0.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.3 GB host RAM)

Decode

19.1 tok/s

TTFT

10156 ms

Safe context

12K

Memory

8.5 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B 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: 19.1 tok/s decode · 10.2s TTFT (warm) · 48 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 {ram} 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	S	Tight fit	27.1 tok/s	3899 ms	12K
Coding	A	Runs with offload	17.7 tok/s	10918 ms	12K
Agentic Coding	F	Too heavy	11.5 tok/s	24507 ms	12K
Reasoning	A	Runs with offload	17.7 tok/s	12903 ms	12K
RAG	F	Too heavy	11.5 tok/s	30634 ms	12K

Quantization options

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

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	S89
Q3_K_S	3	3.9 GB	Low	S88
NVFP4	4

Get started

Copy-paste commands to run Nemotron Nano 8B on your machine.

Run

lms load Llama-3.1-Nemotron-Nano-8B-v1 && lms server start

Your hardware

More models your Radeon Pro W7500 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	A	13.9 tok/s

Frequently asked questions

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

Yes, Radeon Pro W7500 8GB can run Nemotron Nano 8B with a A grade (Runs with offload). Expected decode speed: 17.7 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 8B?

The recommended quantization for Nemotron Nano 8B is Q4_K_M, which balances quality and memory efficiency.

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

On Radeon Pro W7500 8GB, Nemotron Nano 8B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10918ms using Q4_K_M quantization.

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

For coding workloads, Nemotron Nano 8B on Radeon Pro W7500 8GB receives a A grade with 17.7 tok/s and 12K context.

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

On Radeon Pro W7500 8GB, Nemotron Nano 8B can safely use up to 12K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

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

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

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