Can Nemotron Cascade 2 30B A3B run on NVIDIA A100 40GB?

YES — Runs Great

S94Excellent

Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~26.4 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~188 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) — 26.4 GB, 201.9 tok/s, Runs well

26.4 GB required40.0 GB available

66% VRAM used

Fit status

Runs well

Decode

201.9 tok/s

TTFT

959 ms

Safe context

90K

Memory

26.4 GB / 40.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsNemotron Cascade 2 30B A3B on NVIDIA A100 40GB
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: 201.9 tok/s decode · 959ms TTFT (warm) · 505 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	201.9 tok/s	523 ms	90K
Coding	S	Runs well	187.8 tok/s	1031 ms	90K
Agentic Coding	S	Runs well	201.9 tok/s	1395 ms	90K
Reasoning	S	Runs well	201.9 tok/s	1133 ms	90K
RAG	S	Runs well	201.9 tok/s	1743 ms	90K

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A83
Q3_K_S	3	14.7 GB	Low	A84
NVFP4	4

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 NVIDIA A100 40GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
Qwen 3.6 35B A3B	35B	S	166 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run Nemotron Cascade 2 30B A3B?

Yes, NVIDIA A100 40GB can run Nemotron Cascade 2 30B A3B with a S grade (Runs well). Expected decode speed: 187.8 tok/s.

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

Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 26.4 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 NVIDIA A100 40GB?

On NVIDIA A100 40GB, Nemotron Cascade 2 30B A3B achieves approximately 187.8 tokens per second decode speed with a time-to-first-token of 1031ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Nemotron Cascade 2 30B A3B for coding?

For coding workloads, Nemotron Cascade 2 30B A3B on NVIDIA A100 40GB receives a S grade with 187.8 tok/s and 90K context.

What context window can Nemotron Cascade 2 30B A3B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Nemotron Cascade 2 30B A3B can safely use up to 90K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Nemotron Cascade 2 30B A3B

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