Can Nemotron Cascade 2 30B A3B run on RX 9070 XT 16GB?

YES — With Q2_K

A80Great

Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~17.1 GB VRAM. RX 9070 XT 16GB has 16.0 GB. With Q2_K quantization, expect ~56 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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 Cascade 2 30B A3B at Q4_K_M needs 23.7 GB — too much for RX 9070 XT 16GB (16.0 GB). Runs at Q2_K (17.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 23.7 GB, exceeds 16.0 GB available

23.7 GB required16.0 GB available

148% VRAM needed

7.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

21.7 tok/s

TTFT

8934 ms

Safe context

Memory

23.7 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights18.3 GB

KV Cache2.9 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 Cascade 2 30B A3B on RX 9070 XT 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: 21.7 tok/s decode · 8.9s TTFT (warm) · 54 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 0.8 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	F	Too heavy	24.7 tok/s	4279 ms	4K
Coding	F	Too heavy	21.7 tok/s	8934 ms	4K
Agentic Coding	F	Too heavy	17.1 tok/s	16479 ms	4K
Reasoning	F	Too heavy	21.7 tok/s	10559 ms	4K
RAG	F	Too heavy	17.1 tok/s	20599 ms	4K

Inference speed

Nemotron Cascade 2 30B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nemotron Cascade 2 30B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~186 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	185.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	86.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	84.8	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	76.5	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	72.6	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	71.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	68.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	37.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	34.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	32.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	30.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	10.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	6.6	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.6	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 Cascade 2 30B A3B (30B params) fits at each quantization level on RX 9070 XT 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 Cascade 2 30B A3B on your machine.

Run

ollama run nemotron-cascade-2

Opciones de mejora

Hardware que ejecuta bien Nemotron Cascade 2 30B A3B

RX 7900 XTX 24GBOpción económica

24 GB VRAM (+8)960 GB/s (+320)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.76.5 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 32GBMejor relación calidad-precio

32 GB VRAM (+16)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.58.4 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

Radeon Pro W6800 32GBMejora AMD

32 GB VRAM (+16)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.44.3 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.

~$2,249 MSRP

Frequently asked questions

Can RX 9070 XT 16GB run Nemotron Cascade 2 30B A3B?

Yes, RX 9070 XT 16GB can run Nemotron Cascade 2 30B A3B at Q2_K quantization (Runs with offload (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 23.7 GB which exceeds available memory, but at Q2_K it needs only 17.1 GB. Expected decode speed: 56.0 tok/s.

How much VRAM does Nemotron Cascade 2 30B A3B need?

Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 23.7 GB at Q4_K_M quantization. On RX 9070 XT 16GB, it fits at Q2_K using 17.1 GB.

What is the best quantization for Nemotron Cascade 2 30B A3B?

The recommended quantization is Q4_K_M, but on RX 9070 XT 16GB the best fitting quantization is Q2_K, which uses 17.1 GB.

What speed will Nemotron Cascade 2 30B A3B run at on RX 9070 XT 16GB?

On RX 9070 XT 16GB, Nemotron Cascade 2 30B A3B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q2_K quantization.

Can RX 9070 XT 16GB run Nemotron Cascade 2 30B A3B for coding?

For coding workloads, Nemotron Cascade 2 30B A3B on RX 9070 XT 16GB receives a F grade with 21.7 tok/s and 4K context.

What context window can Nemotron Cascade 2 30B A3B use on RX 9070 XT 16GB?

On RX 9070 XT 16GB, Nemotron Cascade 2 30B A3B can safely use up to 10K tokens of context at Q2_K quantization. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron Cascade 2 30B A3B feels slow on RX 9070 XT 16GB?

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 RX 9070 XT 16GB See all hardware for Nemotron Cascade 2 30B A3B

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