Can Nemotron Nano 8B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

A84Great

Estimated from fit model

Nemotron Nano 8B needs ~10.9 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~83 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) — 10.9 GB, 83.2 tok/s, Runs well

10.9 GB required32.0 GB available

34% VRAM used

Fit status

Runs well

Decode

83.2 tok/s

TTFT

2327 ms

Safe context

131K

Memory

10.9 GB / 32.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on Radeon AI PRO R9700 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: 83.2 tok/s decode · 2.3s TTFT (warm) · 208 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	83.2 tok/s	1270 ms	131K
Coding	A	Runs well	83.2 tok/s	2327 ms	131K
Agentic Coding	S	Runs well	83.2 tok/s	3385 ms	131K
Reasoning	A	Runs well	83.2 tok/s	2751 ms	131K
RAG	S	Runs well	83.2 tok/s	4232 ms	131K

Inference speed

Nemotron Nano 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nemotron Nano 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	102.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	96.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	89.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	89.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	83.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	52.9	Fits
RTX 3060 12GB	12 GB	Q4_K_M	52.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	48.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	46.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	26.6	Heavy offload

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A78
Q3_K_S	3	3.9 GB	Low	A78
NVFP4	4	4.5 GB	Medium	A79
Q4_K_M	4	4.9 GB	Medium	A79
Q5_K_M	5	5.8 GB	High	A79
Q6_K	6	6.6 GB	High	A79
Q8_0	8	8.6 GB	Very High	A80
F16Best for your GPU	16	16.4 GB	Maximum	A84

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 AI PRO R9700 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	57.1 tok/s
Qwen 3.5 27B	27B	S	24.8 tok/s
Qwen 3.6 27B	27B	S	18.8 tok/s
Qwen 3.6 35B A3B	35B	S	48 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	59.1 tok/s

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Nemotron Nano 8B?

Yes, Radeon AI PRO R9700 32GB can run Nemotron Nano 8B with a A grade (Runs well). Expected decode speed: 83.2 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 10.9 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 AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Nemotron Nano 8B achieves approximately 83.2 tokens per second decode speed with a time-to-first-token of 2327ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on Radeon AI PRO R9700 32GB receives a A grade with 83.2 tok/s and 131K context.

What context window can Nemotron Nano 8B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Nemotron Nano 8B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for Nemotron Nano 8B

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