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 ~202 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	201.9 tok/s	959 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	16.8 GB	Medium	A85
Q4_K_M	4	18.3 GB	Medium	S85
Q5_K_M	5	21.6 GB	High	S87
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 NVIDIA A100 40GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	166 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	180.5 tok/s
👁 Alibaba Qwen 3 32B	32B	S	72.8 tok/s
👁 Alibaba Qwen 3 30B A3B	30.5B	S	197.5 tok/s

Frequently asked questions

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

URL: https://willitrunai.com/can-run/nemotron-cascade-2-30b-a3b-on-a100-40gb

⇱ Nemotron Cascade 2 30B A3B on NVIDIA A100 40GB? YES