Can Nemotron 3 Nano 30B run on B100 192GB?

YES — Runs Great

S87Excellent

○Estimated from fit model

Nemotron 3 Nano 30B needs ~41.1 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~395 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) — 41.1 GB, 394.8 tok/s, Runs well

41.1 GB required192.0 GB available

21% VRAM used

Fit status

Runs well

Decode

394.8 tok/s

TTFT

490 ms

Safe context

131K

Memory

41.1 GB / 192.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B on B100 192GB
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: 394.8 tok/s decode · 490ms TTFT (warm) · 987 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	394.8 tok/s	350 ms	131K
Coding	S	Runs well	394.8 tok/s	490 ms	131K
Agentic Coding	S	Runs well	394.8 tok/s	713 ms	131K
Reasoning	S	Runs well	394.8 tok/s	580 ms	131K
RAG	S	Runs well	394.8 tok/s	892 ms	131K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A77
Q3_K_S	3	14.7 GB	Low	A77
NVFP4	4	16.8 GB	Medium	A77
Q4_K_M	4	18.3 GB	Medium	A77
Q5_K_M	5	21.6 GB	High	A78
Q6_K	6	24.6 GB	High	A78
Q8_0	8	32.1 GB	Very High	A79
F16Best for your GPU	16	61.5 GB	Maximum	A82

Get started

Copy-paste commands to run Nemotron 3 Nano 30B on your machine.

Run

ollama run nemotron-nano:30b

Your hardware

More models your B100 192GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	97.4 tok/s
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	1016.1 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	270.2 tok/s
👁 DeepSeek DeepSeek V4 Flash	284B	S	144.8 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	854 tok/s

Frequently asked questions

See all results for B100 192GB See all hardware for Nemotron 3 Nano 30B

URL: https://willitrunai.com/can-run/nemotron-3-nano-30b-on-b100-192gb

⇱ Nemotron 3 Nano 30B on B100 192GB? YES