Can Nemotron 3 Nano 30B run on Radeon RX 7900M 16GB?

YES — With Q2_K

S89Excellent

Estimated from fit model

Nemotron 3 Nano 30B needs ~16.6 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q2_K quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

Nemotron 3 Nano 30B at Q4_K_M needs 23.2 GB — too much for Radeon RX 7900M 16GB (16.0 GB). Runs at Q2_K (16.6 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 23.2 GB, exceeds 16.0 GB available

23.2 GB required16.0 GB available

145% VRAM needed

7.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.8 tok/s

TTFT

28374 ms

Safe context

Memory

23.2 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron 3 Nano 30B on Radeon RX 7900M 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: 6.8 tok/s decode · 28.4s TTFT (warm) · 17 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	7.6 tok/s	13815 ms	4K
Coding	F	Too heavy	6.8 tok/s	28374 ms	4K
Agentic Coding	F	Too heavy	5.5 tok/s	50929 ms	4K
Reasoning	F	Too heavy	6.8 tok/s	33533 ms	4K
RAG	F	Too heavy	5.5 tok/s	63661 ms	4K

Inference speed

Nemotron 3 Nano 30B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nemotron 3 Nano 30B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~71 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	70.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.1	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	32.8	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	32.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	30.3	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	28.0	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	27.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	25.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	24.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	14.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	12.9	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	11.9	Too big
RTX 4070 12GB	12 GB	Q4_K_M	4.2	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.6	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

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 3 Nano 30B (30B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	F0
Q3_K_S	3	14.7 GB	Low	F0
NVFP4	4	16.8 GB	Medium	F0
Q4_K_M	4	18.3 GB	Medium	F0
Q5_K_M	5	21.6 GB	High	F0
Q6_K	6	24.6 GB	High	F0
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Get started

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

Run

ollama run nemotron-nano:30b

Opciones de mejora

Hardware que ejecuta bien Nemotron 3 Nano 30B

RX 7900 XT 20GBMejor relación calidad-precio

20 GB VRAM (+4)800 GB/s (+224)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.14.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 119%.

~$899 MSRP

RX 7900 XTX 24GBOpción económica

24 GB VRAM (+8)960 GB/s (+384)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.30.3 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$999 MSRP

Radeon AI PRO R9700 32GBMejora AMD

32 GB VRAM (+16)640 GB/s (+64)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.22.2 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,899 MSRP

Frequently asked questions

Can Radeon RX 7900M 16GB run Nemotron 3 Nano 30B?

Yes, Radeon RX 7900M 16GB can run Nemotron 3 Nano 30B at Q2_K quantization (Runs with offload (needs ~0.5 GB host RAM)). The recommended Q4_K_M requires 23.2 GB which exceeds available memory, but at Q2_K it needs only 16.6 GB. Expected decode speed: 18.3 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 23.2 GB at Q4_K_M quantization. On Radeon RX 7900M 16GB, it fits at Q2_K using 16.6 GB.

What is the best quantization for Nemotron 3 Nano 30B?

The recommended quantization is Q4_K_M, but on Radeon RX 7900M 16GB the best fitting quantization is Q2_K, which uses 16.6 GB.

What speed will Nemotron 3 Nano 30B run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, Nemotron 3 Nano 30B achieves approximately 18.3 tokens per second decode speed with a time-to-first-token of 10565ms using Q2_K quantization.

Can Radeon RX 7900M 16GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on Radeon RX 7900M 16GB receives a F grade with 6.8 tok/s and 4K context.

What context window can Nemotron 3 Nano 30B use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, Nemotron 3 Nano 30B can safely use up to 12K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron 3 Nano 30B feels slow on Radeon RX 7900M 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for Radeon RX 7900M 16GB See all hardware for Nemotron 3 Nano 30B

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