Can Nemotron Cascade 2 30B A3B run on RTX 6000 Ada 48GB?

YES — Runs Great

S92Excellent

Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~27.2 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~122 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 27.2 GB, 121.7 tok/s, Runs well

27.2 GB required48.0 GB available

57% VRAM used

Fit status

Runs well

Decode

121.7 tok/s

TTFT

1591 ms

Safe context

129K

Memory

27.2 GB / 48.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsNemotron Cascade 2 30B A3B on RTX 6000 Ada 48GB
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: 121.7 tok/s decode · 1.6s TTFT (warm) · 304 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	S	Runs well	121.7 tok/s	868 ms	129K
Coding	S	Runs well	121.7 tok/s	1591 ms	129K
Agentic Coding	S	Runs well	121.7 tok/s	2314 ms	129K
Reasoning	S	Runs well	121.7 tok/s	1880 ms	129K
RAG	S	Runs well	121.7 tok/s	2893 ms	129K

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 RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A81
Q3_K_S	3	14.7 GB	Low	A82
NVFP4	4	16.8 GB	Medium	A83
Q4_K_M	4	18.3 GB	Medium	A83
Q5_K_M	5	21.6 GB	High	A84
Q6_K	6	24.6 GB	High	S86
Q8_0Best for your GPU	8	32.1 GB	Very High	S86
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

Your hardware

More models your RTX 6000 Ada 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	119 tok/s
Qwen 3.6 35B A3B	35B	S	100 tok/s
Qwen 3.5 35B A3B	35B	S	108.8 tok/s
Qwen 3 32B	32B	S	43.9 tok/s
Qwen 3 30B A3B	30.5B	S	119 tok/s

Frequently asked questions

Can RTX 6000 Ada 48GB run Nemotron Cascade 2 30B A3B?

Yes, RTX 6000 Ada 48GB can run Nemotron Cascade 2 30B A3B with a S grade (Runs well). Expected decode speed: 121.7 tok/s.

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

Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 27.2 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Nemotron Cascade 2 30B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Cascade 2 30B A3B run at on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Nemotron Cascade 2 30B A3B achieves approximately 121.7 tokens per second decode speed with a time-to-first-token of 1591ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run Nemotron Cascade 2 30B A3B for coding?

For coding workloads, Nemotron Cascade 2 30B A3B on RTX 6000 Ada 48GB receives a S grade with 121.7 tok/s and 129K context.

What context window can Nemotron Cascade 2 30B A3B use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Nemotron Cascade 2 30B A3B can safely use up to 129K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for RTX 6000 Ada 48GB See all hardware for Nemotron Cascade 2 30B A3B

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<iframe src="https://willitrunai.com/embed/nemotron-cascade-2-30b-a3b-on-rtx-6000-ada-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

121.7 tok/s

868 ms

129K

Coding

Runs well

121.7 tok/s

1591 ms

129K

Agentic Coding

Runs well

121.7 tok/s

2314 ms

129K

Reasoning

Runs well

121.7 tok/s

1880 ms

129K

RAG

Runs well

121.7 tok/s

2893 ms

129K

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

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

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A81
Q3_K_S	3	14.7 GB	Low	A82
NVFP4	4	16.8 GB	Medium	A83
Q4_K_M	4	18.3 GB	Medium	A83
Q5_K_M	5	21.6 GB	High	A84
Q6_K	6	24.6 GB	High	S86
Q8_0Best for your GPU	8	32.1 GB	Very High	S86
F16	16	61.5 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

119 tok/s

Qwen 3.6 35B A3B

35B

100 tok/s

Qwen 3.5 35B A3B

35B

108.8 tok/s

Qwen 3 32B

32B

43.9 tok/s

Qwen 3 30B A3B