Can Nemotron Cascade 2 30B A3B run on NVIDIA H800 80GB?

YES — Runs Great

S88Excellent

○Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~30.4 GB VRAM. NVIDIA H800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~376 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) — 30.4 GB, 375.6 tok/s, Runs well

30.4 GB required80.0 GB available

38% VRAM used

Fit status

Runs well

Decode

375.6 tok/s

TTFT

515 ms

Safe context

262K

Memory

30.4 GB / 80.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsNemotron Cascade 2 30B A3B on NVIDIA H800 80GB
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: 375.6 tok/s decode · 515ms TTFT (warm) · 939 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	375.6 tok/s	350 ms	262K
Coding	S	Runs well	375.6 tok/s	515 ms	262K
Agentic Coding	S	Runs well	375.6 tok/s	750 ms	262K
Reasoning	S	Runs well	375.6 tok/s	609 ms	262K
RAG	S	Runs well	375.6 tok/s	937 ms	262K

Quantization options

How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on NVIDIA H800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A78
Q3_K_S	3	14.7 GB	Low	A79
NVFP4	4	16.8 GB	Medium	A79
Q4_K_M	4	18.3 GB	Medium	A79
Q5_K_M	5	21.6 GB	High	A80
Q6_K	6	24.6 GB	High	A80
Q8_0	8	32.1 GB	Very High	A82
F16Best for your GPU	16	61.5 GB	Maximum	S86

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 H800 80GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	A	24.9 tok/s
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	367.4 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	73.9 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	308.8 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	335.8 tok/s

Frequently asked questions

See all results for NVIDIA H800 80GB See all hardware for Nemotron Cascade 2 30B A3B

URL: https://willitrunai.com/can-run/nemotron-cascade-2-30b-a3b-on-h800-80gb

⇱ Nemotron Cascade 2 30B A3B on NVIDIA H800 80GB? YES